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The Cochrane Database of Systematic... May 2022Complex regional pain syndrome (CRPS) is a painful and disabling condition that usually manifests in response to trauma or surgery and is associated with significant... (Review)
Review
BACKGROUND
Complex regional pain syndrome (CRPS) is a painful and disabling condition that usually manifests in response to trauma or surgery and is associated with significant pain and disability. CRPS can be classified into two types: type I (CRPS I) in which a specific nerve lesion has not been identified and type II (CRPS II) where there is an identifiable nerve lesion. Guidelines recommend the inclusion of a variety of physiotherapy interventions as part of the multimodal treatment of people with CRPS. This is the first update of the review originally published in Issue 2, 2016.
OBJECTIVES
To determine the effectiveness of physiotherapy interventions for treating pain and disability associated with CRPS types I and II in adults.
SEARCH METHODS
For this update we searched CENTRAL (the Cochrane Library), MEDLINE, Embase, CINAHL, PsycINFO, LILACS, PEDro, Web of Science, DARE and Health Technology Assessments from February 2015 to July 2021 without language restrictions, we searched the reference lists of included studies and we contacted an expert in the field. We also searched additional online sources for unpublished trials and trials in progress.
SELECTION CRITERIA
We included randomised controlled trials (RCTs) of physiotherapy interventions compared with placebo, no treatment, another intervention or usual care, or other physiotherapy interventions in adults with CRPS I and II. Primary outcomes were pain intensity and disability. Secondary outcomes were composite scores for CRPS symptoms, health-related quality of life (HRQoL), patient global impression of change (PGIC) scales and adverse effects.
DATA COLLECTION AND ANALYSIS
Two review authors independently screened database searches for eligibility, extracted data, evaluated risk of bias and assessed the certainty of evidence using the GRADE system.
MAIN RESULTS
We included 16 new trials (600 participants) along with the 18 trials from the original review totalling 34 RCTs (1339 participants). Thirty-three trials included participants with CRPS I and one trial included participants with CRPS II. Included trials compared a diverse range of interventions including physical rehabilitation, electrotherapy modalities, cortically directed rehabilitation, electroacupuncture and exposure-based approaches. Most interventions were tested in small, single trials. Most were at high risk of bias overall (27 trials) and the remainder were at 'unclear' risk of bias (seven trials). For all comparisons and outcomes where we found evidence, we graded the certainty of the evidence as very low, downgraded due to serious study limitations, imprecision and inconsistency. Included trials rarely reported adverse effects. Physiotherapy compared with minimal care for adults with CRPS I One trial (135 participants) of multimodal physiotherapy, for which pain data were unavailable, found no between-group differences in pain intensity at 12-month follow-up. Multimodal physiotherapy demonstrated a small between-group improvement in disability at 12 months follow-up compared to an attention control (Impairment Level Sum score, 5 to 50 scale; mean difference (MD) -3.7, 95% confidence interval (CI) -7.13 to -0.27) (very low-certainty evidence). Equivalent data for pain were not available. Details regarding adverse events were not reported. Physiotherapy compared with minimal care for adults with CRPS II We did not find any trials of physiotherapy compared with minimal care for adults with CRPS II.
AUTHORS' CONCLUSIONS
The evidence is very uncertain about the effects of physiotherapy interventions on pain and disability in CRPS. This conclusion is similar to our 2016 review. Large-scale, high-quality RCTs with longer-term follow-up are required to test the effectiveness of physiotherapy-based interventions for treating pain and disability in adults with CRPS I and II.
Topics: Adult; Complex Regional Pain Syndromes; Electric Stimulation Therapy; Humans; Pain; Pain Measurement; Physical Therapy Modalities
PubMed: 35579382
DOI: 10.1002/14651858.CD010853.pub3 -
Journal of the American Medical... Jan 2022To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis.
OBJECTIVE
To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis.
MATERIALS AND METHODS
PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted.
RESULTS
The clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies.
DISCUSSION
Studies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units.
CONCLUSIONS
Approaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.
Topics: Humans; Machine Learning; Natural Language Processing; Sepsis; Shock, Septic; Vital Signs
PubMed: 34897469
DOI: 10.1093/jamia/ocab236 -
The Cochrane Database of Systematic... Jun 2021It remains unclear whether people with non-muscle invasive bladder cancer (NMIBC) benefit from intravesical gemcitabine compared to other agents in the primary or... (Meta-Analysis)
Meta-Analysis
BACKGROUND
It remains unclear whether people with non-muscle invasive bladder cancer (NMIBC) benefit from intravesical gemcitabine compared to other agents in the primary or recurrent setting following transurethral resection of a bladder tumor. This is an update of a Cochrane Review first published in 2012. Since that time, several randomized controlled trials (RCTs) have been reported, making this update relevant. OBJECTIVES: To assess the comparative effectiveness and toxicity of intravesical gemcitabine instillation for NMIBC.
SEARCH METHODS
We performed a comprehensive literature search of the Cochrane Library, MEDLINE, Embase, four other databases, trial registries, and conference proceedings to 11 September 2020, with no restrictions on the language or status of publication.
SELECTION CRITERIA
We included RCTs in which participants received intravesical gemcitabine for primary or recurrent NMIBC.
DATA COLLECTION AND ANALYSIS
Two review authors independently assessed the included studies and extracted data for the primary outcomes: time to recurrence, time to progression, grade III to V adverse events determined by the Common Terminology Criteria for Adverse Events version 5.0 (CTCAE v5.0), and the secondary outcomes: time to death from bladder cancer, time to death from any cause, grade I or II adverse events determined by the CTCAE v5.0 and disease-specific quality of life. We performed statistical analyses using a random-effects model and rated the certainty of the evidence using GRADE.
MAIN RESULTS
We included seven studies with 1222 participants with NMIBC across five comparisons. This abstract focuses on the primary outcomes of the three most clinically relevant comparisons. 1. Gemcitabine versus saline: based on two years' to four years' follow-up, gemcitabine may reduce the risk of recurrence over time compared to saline (39% versus 47% recurrence rate, hazard ratio [HR] 0.77, 95% confidence interval [CI] 0.54 to 1.09; studies = 2, participants = 734; I = 49%; low-certainty evidence), but the CI included the possibility of no effect. Gemcitabine may result in little to no difference in the risk of progression over time compared to saline (4.6% versus 4.8% progression rate, HR 0.96, 95% CI 0.19 to 4.71; studies = 2, participants = 654; I = 53%; low-certainty evidence). Gemcitabine may result in little to no difference in the CTCAE grade III to V adverse events compared to saline (5.9% versus 4.7% adverse events rate, risk ratio [RR] 1.26, 95% CI 0.58 to 2.75; studies = 2, participants = 668; I = 24%; low-certainty evidence). 2. Gemcitabine versus mitomycin: based on three years' follow-up (studies = 1, participants = 109), gemcitabine may reduce the risk of recurrence over time compared to mitomycin (17% versus 40% recurrence rate, HR 0.36, 95% CI 0.19 to 0.69; low-certainty evidence). Gemcitabine may reduce the risk of progression over time compared to mitomycin (11% versus 18% progression rate, HR 0.57, 95% CI 0.32 to 1.01; low-certainty evidence), but the CI included the possibility of no effect. We are very uncertain about the effect of gemcitabine on the CTCAE grade III to V adverse events compared to mitomycin (RR 0.51, 95% CI 0.13 to 1.93; very low-certainty evidence). The analysis was only based on recurrent NMIBC. 3. Gemcitabine versus Bacillus Calmette-Guérin (BCG) for recurrent (one-course BCG failure) high-risk NMIBC: based on 6 months' to 22 months' follow-up (studies = 1, participants = 80), gemcitabine may reduce the risk of recurrence compared to BCG (41% versus 97% recurrence rate, HR 0.15, 95% CI 0.09 to 0.26; low-certainty evidence) and progression over time (16% versus 33% progression rate, HR 0.45, 95% CI 0.27 to 0.76; low-certainty evidence). We are very uncertain about the effect of gemcitabine on the CTCAE grade III to V adverse events compared to BCG (RR 1.00, 95% CI 0.21 to 4.66; very low-certainty evidence). In addition, the review provides information on the comparison of gemcitabine versus BCG and gemcitabine versus one-third dose BCG. AUTHORS' CONCLUSIONS: Based on findings of this review, gemcitabine may have a more favorable impact on recurrence and progression-free survival than mitomycin but we are very uncertain as to how major adverse events compare. The same is true when comparing gemcitabine to BCG in individuals with high risk disease who have previously failed BCG. The underlying low- to very low-certainty evidence indicates that our confidence in these results is limited; the true effects may be substantially different from these findings; therefore, better quality studies are needed.
Topics: Adjuvants, Immunologic; Administration, Intravesical; Antibiotics, Antineoplastic; Antimetabolites, Antineoplastic; BCG Vaccine; Bias; Cause of Death; Confidence Intervals; Deoxycytidine; Disease Progression; Drug Administration Schedule; Humans; Mitomycin; Neoplasm Recurrence, Local; Randomized Controlled Trials as Topic; Saline Solution; Urinary Bladder Neoplasms; Gemcitabine
PubMed: 34125951
DOI: 10.1002/14651858.CD009294.pub3 -
Sensors (Basel, Switzerland) Oct 2023The analysis and recognition of sign languages are currently active fields of research focused on sign recognition. Various approaches differ in terms of analysis... (Review)
Review
The analysis and recognition of sign languages are currently active fields of research focused on sign recognition. Various approaches differ in terms of analysis methods and the devices used for sign acquisition. Traditional methods rely on video analysis or spatial positioning data calculated using motion capture tools. In contrast to these conventional recognition and classification approaches, electromyogram (EMG) signals, which measure muscle electrical activity, offer potential technology for detecting gestures. These EMG-based approaches have recently gained attention due to their advantages. This prompted us to conduct a comprehensive study on the methods, approaches, and projects utilizing EMG sensors for sign language handshape recognition. In this paper, we provided an overview of the sign language recognition field through a literature review, with the objective of offering an in-depth review of the most significant techniques. These techniques were categorized in this article based on their respective methodologies. The survey discussed the progress and challenges in sign language recognition systems based on surface electromyography (sEMG) signals. These systems have shown promise but face issues like sEMG data variability and sensor placement. Multiple sensors enhance reliability and accuracy. Machine learning, including deep learning, is used to address these challenges. Common classifiers in sEMG-based sign language recognition include SVM, ANN, CNN, KNN, HMM, and LSTM. While SVM and ANN are widely used, random forest and KNN have shown better performance in some cases. A multilayer perceptron neural network achieved perfect accuracy in one study. CNN, often paired with LSTM, ranks as the third most popular classifier and can achieve exceptional accuracy, reaching up to 99.6% when utilizing both EMG and IMU data. LSTM is highly regarded for handling sequential dependencies in EMG signals, making it a critical component of sign language recognition systems. In summary, the survey highlights the prevalence of SVM and ANN classifiers but also suggests the effectiveness of alternative classifiers like random forests and KNNs. LSTM emerges as the most suitable algorithm for capturing sequential dependencies and improving gesture recognition in EMG-based sign language recognition systems.
Topics: Humans; Sign Language; Reproducibility of Results; Pattern Recognition, Automated; Neural Networks, Computer; Algorithms; Electromyography; Gestures
PubMed: 37837173
DOI: 10.3390/s23198343 -
The Cochrane Database of Systematic... Feb 2022Description of the condition Malaria, an infectious disease transmitted by the bite of female mosquitoes from several Anopheles species, occurs in 87 countries with... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Description of the condition Malaria, an infectious disease transmitted by the bite of female mosquitoes from several Anopheles species, occurs in 87 countries with ongoing transmission (WHO 2020). The World Health Organization (WHO) estimated that, in 2019, approximately 229 million cases of malaria occurred worldwide, with 94% occurring in the WHO's African region (WHO 2020). Of these malaria cases, an estimated 409,000 deaths occurred globally, with 67% occurring in children under five years of age (WHO 2020). Malaria also negatively impacts the health of women during pregnancy, childbirth, and the postnatal period (WHO 2020). Sulfadoxine/pyrimethamine (SP), an antifolate antimalarial, has been widely used across sub-Saharan Africa as the first-line treatment for uncomplicated malaria since it was first introduced in Malawi in 1993 (Filler 2006). Due to increasing resistance to SP, in 2000 the WHO recommended that one of several artemisinin-based combination therapies (ACTs) be used instead of SP for the treatment of uncomplicated malaria caused by Plasmodium falciparum (Global Partnership to Roll Back Malaria 2001). However, despite these recommendations, SP continues to be advised for intermittent preventive treatment in pregnancy (IPTp) and intermittent preventive treatment in infants (IPTi), whether the person has malaria or not (WHO 2013). Description of the intervention Folate (vitamin B9) includes both naturally occurring folates and folic acid, the fully oxidized monoglutamic form of the vitamin, used in dietary supplements and fortified food. Folate deficiency (e.g. red blood cell (RBC) folate concentrations of less than 305 nanomoles per litre (nmol/L); serum or plasma concentrations of less than 7 nmol/L) is common in many parts of the world and often presents as megaloblastic anaemia, resulting from inadequate intake, increased requirements, reduced absorption, or abnormal metabolism of folate (Bailey 2015; WHO 2015a). Pregnant women have greater folate requirements; inadequate folate intake (evidenced by RBC folate concentrations of less than 400 nanograms per millilitre (ng/mL), or 906 nmol/L) prior to and during the first month of pregnancy increases the risk of neural tube defects, preterm delivery, low birthweight, and fetal growth restriction (Bourassa 2019). The WHO recommends that all women who are trying to conceive consume 400 micrograms (µg) of folic acid daily from the time they begin trying to conceive through to 12 weeks of gestation (WHO 2017). In 2015, the WHO added the dosage of 0.4 mg of folic acid to the essential drug list (WHO 2015c). Alongside daily oral iron (30 mg to 60 mg elemental iron), folic acid supplementation is recommended for pregnant women to prevent neural tube defects, maternal anaemia, puerperal sepsis, low birthweight, and preterm birth in settings where anaemia in pregnant women is a severe public health problem (i.e. where at least 40% of pregnant women have a blood haemoglobin (Hb) concentration of less than 110 g/L). How the intervention might work Potential interactions between folate status and malaria infection The malaria parasite requires folate for survival and growth; this has led to the hypothesis that folate status may influence malaria risk and severity. In rhesus monkeys, folate deficiency has been found to be protective against Plasmodium cynomolgi malaria infection, compared to folate-replete animals (Metz 2007). Alternatively, malaria may induce or exacerbate folate deficiency due to increased folate utilization from haemolysis and fever. Further, folate status measured via RBC folate is not an appropriate biomarker of folate status in malaria-infected individuals since RBC folate values in these individuals are indicative of both the person's stores and the parasite's folate synthesis. A study in Nigeria found that children with malaria infection had significantly higher RBC folate concentrations compared to children without malaria infection, but plasma folate levels were similar (Bradley-Moore 1985). Why it is important to do this review The malaria parasite needs folate for survival and growth in humans. For individuals, adequate folate levels are critical for health and well-being, and for the prevention of anaemia and neural tube defects. Many countries rely on folic acid supplementation to ensure adequate folate status in at-risk populations. Different formulations for folic acid supplements are available in many international settings, with dosages ranging from 400 µg to 5 mg. Evaluating folic acid dosage levels used in supplementation efforts may increase public health understanding of its potential impacts on malaria risk and severity and on treatment failures. Examining folic acid interactions with antifolate antimalarial medications and with malaria disease progression may help countries in malaria-endemic areas determine what are the most appropriate lower dose folic acid formulations for at-risk populations. The WHO has highlighted the limited evidence available and has indicated the need for further research on biomarkers of folate status, particularly interactions between RBC folate concentrations and tuberculosis, human immunodeficiency virus (HIV), and antifolate antimalarial drugs (WHO 2015b). An earlier Cochrane Review assessed the effects and safety of iron supplementation, with or without folic acid, in children living in hyperendemic or holoendemic malaria areas; it demonstrated that iron supplementation did not increase the risk of malaria, as indicated by fever and the presence of parasites in the blood (Neuberger 2016). Further, this review stated that folic acid may interfere with the efficacy of SP; however, the efficacy and safety of folic acid supplementation on these outcomes has not been established. This review will provide evidence on the effectiveness of daily folic acid supplementation in healthy and malaria-infected individuals living in malaria-endemic areas. Additionally, it will contribute to achieving both the WHO Global Technical Strategy for Malaria 2016-2030 (WHO 2015d), and United Nations Sustainable Development Goal 3 (to ensure healthy lives and to promote well-being for all of all ages) (United Nations 2021), and evaluating whether the potential effects of folic acid supplementation, at different doses (e.g. 0.4 mg, 1 mg, 5 mg daily), interferes with the effect of drugs used for prevention or treatment of malaria.
OBJECTIVES
To examine the effects of folic acid supplementation, at various doses, on malaria susceptibility (risk of infection) and severity among people living in areas with various degrees of malaria endemicity. We will examine the interaction between folic acid supplements and antifolate antimalarial drugs. Specifically, we will aim to answer the following. Among uninfected people living in malaria endemic areas, who are taking or not taking antifolate antimalarials for malaria prophylaxis, does taking a folic acid-containing supplement increase susceptibility to or severity of malaria infection? Among people with malaria infection who are being treated with antifolate antimalarials, does folic acid supplementation increase the risk of treatment failure?
METHODS
Criteria for considering studies for this review Types of studies Inclusion criteria Randomized controlled trials (RCTs) Quasi-RCTs with randomization at the individual or cluster level conducted in malaria-endemic areas (areas with ongoing, local malaria transmission, including areas approaching elimination, as listed in the World Malaria Report 2020) (WHO 2020) Exclusion criteria Ecological studies Observational studies In vivo/in vitro studies Economic studies Systematic literature reviews and meta-analyses (relevant systematic literature reviews and meta-analyses will be excluded but flagged for grey literature screening) Types of participants Inclusion criteria Individuals of any age or gender, living in a malaria endemic area, who are taking antifolate antimalarial medications (including but not limited to sulfadoxine/pyrimethamine (SP), pyrimethamine-dapsone, pyrimethamine, chloroquine and proguanil, cotrimoxazole) for the prevention or treatment of malaria (studies will be included if more than 70% of the participants live in malaria-endemic regions) Studies assessing participants with or without anaemia and with or without malaria parasitaemia at baseline will be included Exclusion criteria Individuals not taking antifolate antimalarial medications for prevention or treatment of malaria Individuals living in non-malaria endemic areas Types of interventions Inclusion criteria Folic acid supplementation Form: in tablet, capsule, dispersible tablet at any dose, during administration, or periodically Timing: during, before, or after (within a period of four to six weeks) administration of antifolate antimalarials Iron-folic acid supplementation Folic acid supplementation in combination with co-interventions that are identical between the intervention and control groups. Co-interventions include: anthelminthic treatment; multivitamin or multiple micronutrient supplementation; 5-methyltetrahydrofolate supplementation. Exclusion criteria Folate through folate-fortified water Folic acid administered through large-scale fortification of rice, wheat, or maize Comparators Placebo No treatment No folic acid/different doses of folic acid Iron Types of outcome measures Primary outcomes Uncomplicated malaria (defined as a history of fever with parasitological confirmation; acceptable parasitological confirmation will include rapid diagnostic tests (RDTs), malaria smears, or nucleic acid detection (i.e. polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), etc.)) (WHO 2010). This outcome is relevant for patients without malaria, given antifolate antimalarials for malaria prophylaxis. Severe malaria (defined as any case with cerebral malaria or acute P. falciparum malaria, with signs of severity or evidence of vital organ dysfunction, or both) (WHO 2010). This outcome is relevant for patients without malaria, given antifolate antimalarials for malaria prophylaxis. Parasite clearance (any Plasmodium species), defined as the time it takes for a patient who tests positive at enrolment and is treated to become smear-negative or PCR negative. This outcome is relevant for patients with malaria, treated with antifolate antimalarials. Treatment failure (defined as the inability to clear malaria parasitaemia or prevent recrudescence after administration of antimalarial medicine, regardless of whether clinical symptoms are resolved) (WHO 2019). This outcome is relevant for patients with malaria, treated with antifolate antimalarials. Secondary outcomes Duration of parasitaemia Parasite density Haemoglobin (Hb) concentrations (g/L) Anaemia: severe anaemia (defined as Hb less than 70 g/L in pregnant women and children aged six to 59 months; and Hb less than 80 g/L in other populations); moderate anaemia (defined as Hb less than 100 g/L in pregnant women and children aged six to 59 months; and less than 110 g/L in others) Death from any cause Among pregnant women: stillbirth (at less than 28 weeks gestation); low birthweight (less than 2500 g); active placental malaria (defined as Plasmodium detected in placental blood by smear or PCR, or by Plasmodium detected on impression smear or placental histology). Search methods for identification of studies A search will be conducted to identify completed and ongoing studies, without date or language restrictions. Electronic searches A search strategy will be designed to include the appropriate subject headings and text word terms related to each intervention of interest and study design of interest (see Appendix 1). Searches will be broken down by these two criteria (intervention of interest and study design of interest) to allow for ease of prioritization, if necessary. The study design filters recommended by the Scottish Intercollegiate Guidelines Network (SIGN), and those designed by Cochrane for identifying clinical trials for MEDLINE and Embase, will be used (SIGN 2020). There will be no date or language restrictions. Non-English articles identified for inclusion will be translated into English. If translations are not possible, advice will be requested from the Cochrane Infectious Diseases Group and the record will be stored in the "Awaiting assessment" section of the review until a translation is available. The following electronic databases will be searched for primary studies. Cochrane Central Register of Controlled Trials. Cumulative Index to Nursing and Allied Health Literature (CINAHL). Embase. MEDLINE. Scopus. Web of Science (both the Social Science Citation Index and the Science Citation Index). We will conduct manual searches of ClinicalTrials.gov, the International Clinical Trials Registry Platform (ICTRP), and the United Nations Children's Fund (UNICEF) Evaluation and Research Database (ERD), in order to identify relevant ongoing or planned trials, abstracts, and full-text reports of evaluations, studies, and surveys related to programmes on folic acid supplementation in malaria-endemic areas. Additionally, manual searches of grey literature to identify RCTs that have not yet been published but are potentially eligible for inclusion will be conducted in the following sources. Global Index Medicus (GIM). African Index Medicus (AIM). Index Medicus for the Eastern Mediterranean Region (IMEMR). Latin American & Caribbean Health Sciences Literature (LILACS). Pan American Health Organization (PAHO). Western Pacific Region Index Medicus (WPRO). Index Medicus for the South-East Asian Region (IMSEAR). The Spanish Bibliographic Index in Health Sciences (IBECS) (ibecs.isciii.es/). Indian Journal of Medical Research (IJMR) (journals.lww.com/ijmr/pages/default.aspx). Native Health Database (nativehealthdatabase.net/). Scielo (www.scielo.br/). Searching other resources Handsearches of the five journals with the highest number of included studies in the last 12 months will be conducted to capture any relevant articles that may not have been indexed in the databases at the time of the search. We will contact the authors of included studies and will check reference lists of included papers for the identification of additional records. For assistance in identifying ongoing or unpublished studies, we will contact the Division of Nutrition, Physical Activity, and Obesity (DNPAO) and the Division of Parasitic Diseases and Malaria (DPDM) of the CDC, the United Nations World Food Programme (WFP), Nutrition International (NI), Global Alliance for Improved Nutrition (GAIN), and Hellen Keller International (HKI). Data collection and analysis Selection of studies Two review authors will independently screen the titles and abstracts of articles retrieved by each search to assess eligibility, as determined by the inclusion and exclusion criteria. Studies deemed eligible for inclusion by both review authors in the abstract screening phase will advance to the full-text screening phase, and full-text copies of all eligible papers will be retrieved. If full articles cannot be obtained, we will attempt to contact the authors to obtain further details of the studies. If such information is not obtained, we will classify the study as "awaiting assessment" until further information is published or made available to us. The same two review authors will independently assess the eligibility of full-text articles for inclusion in the systematic review. If any discrepancies occur between the studies selected by the two review authors, a third review author will provide arbitration. Each trial will be scrutinized to identify multiple publications from the same data set, and the justification for excluded trials will be documented. A PRISMA flow diagram of the study selection process will be presented to provide information on the number of records identified in the literature searches, the number of studies included and excluded, and the reasons for exclusion (Moher 2009). The list of excluded studies, along with their reasons for exclusion at the full-text screening phase, will also be created. Data extraction and management Two review authors will independently extract data for the final list of included studies using a standardized data specification form. Discrepancies observed between the data extracted by the two authors will be resolved by involving a third review author and reaching a consensus. Information will be extracted on study design components, baseline participant characteristics, intervention characteristics, and outcomes. For individually randomized trials, we will record the number of participants experiencing the event and the number analyzed in each treatment group or the effect estimate reported (e.g. risk ratio (RR)) for dichotomous outcome measures. For count data, we will record the number of events and the number of person-months of follow-up in each group. If the number of person-months is not reported, the product of the duration of follow-up and the number of children evaluated will be used to estimate this figure. We will calculate the rate ratio and standard error (SE) for each study. Zero events will be replaced by 0.5. We will extract both adjusted and unadjusted covariate incidence rate ratios if they are reported in the original studies. For continuous data, we will extract means (arithmetic or geometric) and a measure of variance (standard deviation (SD), SE, or confidence interval (CI)), percentage or mean change from baseline, and the numbers analyzed in each group. SDs will be computed from SEs or 95% CIs, assuming a normal distribution of the values. Haemoglobin values in g/dL will be calculated by multiplying haematocrit or packed cell volume values by 0.34, and studies reporting haemoglobin values in g/dL will be converted to g/L. In cluster-randomized trials, we will record the unit of randomization (e.g. household, compound, sector, or village), the number of clusters in the trial, and the average cluster size. The statistical methods used to analyze the trials will be documented, along with details describing whether these methods adjusted for clustering or other covariates. We plan to extract estimates of the intra-cluster correlation coefficient (ICC) for each outcome. Where results are adjusted for clustering, we will extract the treatment effect estimate and the SD or CI. If the results are not adjusted for clustering, we will extract the data reported. Assessment of risk of bias in included studies Two review authors (KSC, LFY) will independently assess the risk of bias for each included trial using the Cochrane 'Risk of bias 2' tool (RoB 2) for randomized studies (Sterne 2019). Judgements about the risk of bias of included studies will be made according to the recommendations outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). Disagreements will be resolved by discussion, or by involving a third review author. The interest of our review will be to assess the effect of assignment to the interventions at baseline. We will evaluate each primary outcome using the RoB2 tool. The five domains of the Cochrane RoB2 tool include the following. Bias arising from the randomization process. Bias due to deviations from intended interventions. Bias due to missing outcome data. Bias in measurement of the outcome. Bias in selection of the reported result. Each domain of the RoB2 tool comprises the following. A series of 'signalling' questions. A judgement about the risk of bias for the domain, facilitated by an algorithm that maps responses to the signalling questions to a proposed judgement. Free-text boxes to justify responses to the signalling questions and 'Risk of bias' judgements. An option to predict (and explain) the likely direction of bias. Responses to signalling questions elicit information relevant to an assessment of the risk of bias. These response options are as follows. Yes (may indicate either low or high risk of bias, depending on the most natural way to ask the question). Probably yes. Probably no. No. No information (may indicate no evidence of that problem or an absence of information leading to concerns about there being a problem). Based on the answer to the signalling question, a 'Risk of bias' judgement is assigned to each domain. These judgements include one of the following. High risk of bias Low risk of bias Some concerns To generate the risk of bias judgement for each domain in the randomized studies, we will use the Excel template, available at www.riskofbias.info/welcome/rob-2-0-tool/current-version-of-rob-2. This file will be stored on a scientific data website, available to readers. Risk of bias in cluster randomized controlled trials For the cluster randomized trials, we will be using the RoB2 tool to analyze the five standard domains listed above along with Domain 1b (bias arising from the timing of identification or recruitment of participants) and its related signalling questions. To generate the risk of bias judgement for each domain in the cluster RCTs, we will use the Excel template available at https://sites.google.com/site/riskofbiastool/welcome/rob-2-0-tool/rob-2-for-cluster-randomized-trials. This file will be stored on a scientific data website, available to readers. Risk of bias in cross-over randomized controlled trials For cross-over randomized trials, we will be using the RoB2 tool to analyze the five standard domains listed above along with Domain 2 (bias due to deviations from intended interventions), and Domain 3 (bias due to missing outcome data), and their respective signalling questions. To generate the risk of bias judgement for each domain in the cross-over RCTs, we will use the Excel template, available at https://sites.google.com/site/riskofbiastool/welcome/rob-2-0-tool/rob-2-for-crossover-trials, for each risk of bias judgement of cross-over randomized studies. This file will be stored on a scientific data website, available to readers. Overall risk of bias The overall 'Risk of bias' judgement for each specific trial being assessed will be based on each domain-level judgement. The overall judgements include the following. Low risk of bias (the trial is judged to be at low risk of bias for all domains). Some concerns (the trial is judged to raise some concerns in at least one domain but is not judged to be at high risk of bias for any domain). High risk of bias (the trial is judged to be at high risk of bias in at least one domain, or is judged to have some concerns for multiple domains in a way that substantially lowers confidence in the result). The 'risk of bias' assessments will inform our GRADE evaluations of the certainty of evidence for our primary outcomes presented in the 'Summary of findings' tables and will also be used to inform the sensitivity analyses; (see Sensitivity analysis). If there is insufficient information in study reports to enable an assessment of the risk of bias, studies will be classified as "awaiting assessment" until further information is published or made available to us. Measures of treatment effect Dichotomous data For dichotomous data, we will present proportions and, for two-group comparisons, results as average RR or odds ratio (OR) with 95% CIs. Ordered categorical data Continuous data We will report results for continuous outcomes as the mean difference (MD) with 95% CIs, if outcomes are measured in the same way between trials. Where some studies have reported endpoint data and others have reported change-from-baseline data (with errors), we will combine these in the meta-analysis, if the outcomes were reported using the same scale. We will use the standardized mean difference (SMD), with 95% CIs, to combine trials that measured the same outcome but used different methods. If we do not find three or more studies for a pooled analysis, we will summarize the results in a narrative form. Unit of analysis issues Cluster-randomized trials We plan to combine results from both cluster-randomized and individually randomized studies, providing there is little heterogeneity between the studies. If the authors of cluster-randomized trials conducted their analyses at a different level from that of allocation, and they have not appropriately accounted for the cluster design in their analyses, we will calculate the trials' effective sample sizes to account for the effect of clustering in data. When one or more cluster-RCT reports RRs adjusted for clustering, we will compute cluster-adjusted SEs for the other trials. When none of the cluster-RCTs provide cluster-adjusted RRs, we will adjust the sample size for clustering. We will divide, by the estimated design effects (DE), the number of events and number evaluated for dichotomous outcomes and the number evaluated for continuous outcomes, where DE = 1 + ((average cluster size 1) * ICC). The derivation of the estimated ICCs and DEs will be reported. We will utilize the intra-cluster correlation coefficient (ICC), derived from the trial (if available), or from another source (e.g., using the ICCs derived from other, similar trials) and then calculate the design effect with the formula provided in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). If this approach is used, we will report it and undertake sensitivity analysis to investigate the effect of variations in ICC. Studies with more than two treatment groups If we identify studies with more than two intervention groups (multi-arm studies), where possible we will combine groups to create a single pair-wise comparison or use the methods set out in the Cochrane Handbook to avoid double counting study participants (Higgins 2021). For the subgroup analyses, when the control group was shared by two or more study arms, we will divide the control group (events and total population) over the number of relevant subgroups to avoid double counting the participants. Trials with several study arms can be included more than once for different comparisons. Cross-over trials From cross-over trials, we will consider the first period of measurement only and will analyze the results together with parallel-group studies. Multiple outcome events In several outcomes, a participant might experience more than one outcome event during the trial period. For all outcomes, we will extract the number of participants with at least one event. Dealing with missing data We will contact the trial authors if the available data are unclear, missing, or reported in a format that is different from the format needed. We aim to perform a 'per protocol' or 'as observed' analysis; otherwise, we will perform a complete case analysis. This means that for treatment failure, we will base the analyses on the participants who received treatment and the number of participants for which there was an inability to clear malarial parasitaemia or prevent recrudescence after administration of an antimalarial medicine reported in the studies. Assessment of heterogeneity Heterogeneity in the results of the trials will be assessed by visually examining the forest plot to detect non-overlapping CIs, using the Chi2 test of heterogeneity (where a P value of less than 0.1 indicates statistical significance) and the I2 statistic of inconsistency (with a value of greater than 50% denoting moderate levels of heterogeneity). When statistical heterogeneity is present, we will investigate the reasons for it, using subgroup analysis. Assessment of reporting biases We will construct a funnel plot to assess the effect of small studies for the main outcome (when including more than 10 trials). Data synthesis The primary analysis will include all eligible studies that provide data regardless of the overall risk of bias as assessed by the RoB2 tool. Analyses will be conducted using Review Manager 5.4 (Review Manager 2020). Cluster-RCTs will be included in the main analysis after adjustment for clustering (see the previous section on cluster-RCTs). The meta-analysis will be performed using the Mantel-Haenszel random-effects model or the generic inverse variance method (when adjustment for clustering is performed by adjusting SEs), as appropriate. Subgroup analysis and investigation of heterogeneity The overall risk of bias will not be used as the basis in conducting our subgroup analyses. However, where data are available, we plan to conduct the following subgroup analyses, independent of heterogeneity. Dose of folic acid supplementation: higher doses (4 mg or more, daily) versus lower doses (less than 4 mg, daily). Moderate-severe anaemia at baseline (mean haemoglobin of participants in a trial at baseline below 100 g/L for pregnant women and children aged six to 59 months, and below 110 g/L for other populations) versus normal at baseline (mean haemoglobin above 100 g/L for pregnant women and children aged six to 59 months, and above 110 g/L for other populations). Antimalarial drug resistance to parasite: known resistance versus no resistance versus unknown/mixed/unreported parasite resistance. Folate status at baseline: Deficient (e.g. RBC folate concentration of less than 305 nmol/L, or serum folate concentration of less than 7nmol/L) and Insufficient (e.g. RBC folate concentration from 305 to less than 906 nmol/L, or serum folate concentration from 7 to less than 25 nmol/L) versus Sufficient (e.g. RBC folate concentration above 906 nmol/L, or serum folate concentration above 25 nmol/L). Presence of anaemia at baseline: yes versus no. Mandatory fortification status: yes, versus no (voluntary or none). We will only use the primary outcomes in any subgroup analyses, and we will limit subgroup analyses to those outcomes for which three or more trials contributed data. Comparisons between subgroups will be performed using Review Manager 5.4 (Review Manager 2020). Sensitivity analysis We will perform a sensitivity analysis, using the risk of bias as a variable to explore the robustness of the findings in our primary outcomes. We will verify the behaviour of our estimators by adding and removing studies with a high risk of bias overall from the analysis. That is, studies with a low risk of bias versus studies with a high risk of bias. Summary of findings and assessment of the certainty of the evidence For the assessment across studies, we will use the GRADE approach, as outlined in (Schünemann 2021). We will use the five GRADE considerations (study limitations based on RoB2 judgements, consistency of effect, imprecision, indirectness, and publication bias) to assess the certainty of the body of evidence as it relates to the studies which contribute data to the meta-analyses for the primary outcomes. The GRADEpro Guideline Development Tool (GRADEpro) will be used to import data from Review Manager 5.4 (Review Manager 2020) to create 'Summary of Findings' tables. The primary outcomes for the main comparison will be listed with estimates of relative effects, along with the number of participants and studies contributing data for those outcomes. These tables will provide outcome-specific information concerning the overall certainty of evidence from studies included in the comparison, the magnitude of the effect of the interventions examined, and the sum of available data on the outcomes we considered. We will include only primary outcomes in the summary of findings tables. For each individual outcome, two review authors (KSC, LFY) will independently assess the certainty of the evidence using the GRADE approach (Balshem 2011). For assessments of the overall certainty of evidence for each outcome that includes pooled data from included trials, we will downgrade the evidence from 'high certainty' by one level for serious (or by two for very serious) study limitations (risk of bias, indirectness of evidence, serious inconsistency, imprecision of effect estimates, or potential publication bias).
Topics: Child; Infant; Pregnancy; Infant, Newborn; Female; Humans; Child, Preschool; Antimalarials; Sulfadoxine; Pyrimethamine; Folic Acid Antagonists; Birth Weight; Parasitemia; Vitamins; Folic Acid; Anemia; Neural Tube Defects; Dietary Supplements; Iron; Recurrence
PubMed: 36321557
DOI: 10.1002/14651858.CD014217 -
PeerJ. Computer Science 2022Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip...
BACKGROUND AND OBJECTIVE
Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip motion, and many more. Comprehending sign language is just as crucial as learning a natural language. Sign language is the primary mode of communication for those who have a deaf or mute impairment or are disabled. Without a translator, people with auditory difficulties have difficulty speaking with other individuals. Studies in automatic recognition of sign language identification utilizing machine learning techniques have recently shown exceptional success and made significant progress. The primary objective of this research is to conduct a literature review on all the work completed on the recognition of Urdu Sign Language through machine learning classifiers to date.
MATERIALS AND METHODS
All the studies have been extracted from databases, i.e., PubMed, IEEE, Science Direct, and Google Scholar, using a structured set of keywords. Each study has gone through proper screening criteria, , exclusion and inclusion criteria. PRISMA guidelines have been followed and implemented adequately throughout this literature review.
RESULTS
This literature review comprised 20 research articles that fulfilled the eligibility requirements. Only those articles were chosen for additional full-text screening that follows eligibility requirements for peer-reviewed and research articles and studies issued in credible journals and conference proceedings until July 2021. After other screenings, only studies based on Urdu Sign language were included. The results of this screening are divided into two parts; (1) a summary of all the datasets available on Urdu Sign Language. (2) a summary of all the machine learning techniques for recognizing Urdu Sign Language.
CONCLUSION
Our research found that there is only one publicly-available USL sign-based dataset with pictures versus many character-, number-, or sentence-based publicly available datasets. It was also concluded that besides SVM and Neural Network, no unique classifier is used more than once. Additionally, no researcher opted for an unsupervised machine learning classifier for detection. To the best of our knowledge, this is the first literature review conducted on machine learning approaches applied to Urdu sign language.
PubMed: 35494799
DOI: 10.7717/peerj-cs.883 -
AIMS Neuroscience 2021Language processing involves other cognitive domains, including Working Memory (WM). Much detail about the neural correlates of language and WM interaction remains... (Review)
Review
Language processing involves other cognitive domains, including Working Memory (WM). Much detail about the neural correlates of language and WM interaction remains unclear. This review summarizes the evidence for the interaction between WM and language obtained via functional Magnetic Resonance Imaging (fMRI) in the past two decades. The search was limited to PubMed, Google Scholar, Science direct and Neurosynth for working memory, language, fMRI, neuroimaging, cognition, attention, network, connectome keywords. The exclusion criteria consisted of studies including children, older adults, bilingual or multilingual population, clinical cases, music, sign language, speech, motor processing, review papers, meta-analyses, electroencephalography/event-related potential, and positron emission tomography. A total of 20 articles were included and discussed in four categories: language comprehension, language production, syntax, and networks. Studies on neural correlates of WM and language interaction are rare. Language tasks that involve WM activate common neural systems. Activated areas can be associated with cognitive concepts proposed by Baddeley and Hitch (1974), including the phonological loop of WM (mainly Broca and Wernicke's areas), other prefrontal cortex and right hemispheric regions linked to the visuospatial sketchpad. There is a clear, dynamic interaction between language and WM, reflected in the involvement of subcortical structures, particularly the basal ganglia (caudate), and of widespread right hemispheric regions. WM involvement is levered by cognitive demand in response to task complexity. High WM capacity readers draw upon buffer memory systems in midline cortical areas to decrease the WM demands for efficiency. Different dynamic networks are involved in WM and language interaction in response to the task in hand for an ultimate brain function efficiency, modulated by language modality and attention.
PubMed: 33490370
DOI: 10.3934/Neuroscience.2021001 -
Health Literacy Research and Practice Feb 2020Low health literacy is associated with poor health outcomes in many chronic diseases and may have an important role in determining surgical outcomes. This study aims to...
BACKGROUND
Low health literacy is associated with poor health outcomes in many chronic diseases and may have an important role in determining surgical outcomes. This study aims to comprehensively review the current state of science on adult health literacy in surgery and to identify knowledge gaps for future research.
METHODS
Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic search was conducted to identify all studies from January 2002 through May 2018 that used validated instruments to assess health literacy among adult patients undergoing surgery. Studies were assessed for quality using the Newcastle-Ottawa scale and evaluated on findings by their focus on identifying health literacy levels, understanding associations with surgical outcomes, and/or developing interventions to address low health literacy.
KEY RESULTS
There were 51 studies on health literacy with data from 22,139 patients included in this review. Low health literacy was present in more than one-third of surgical patients (34%, interquartile range 16%-50%). The most commonly used validated instrument for assessment of health literacy in the surgical population was the Newest Vital Sign. Most studies were focused on identifying the prevalence of low health literacy within a surgery population (84%, n = 43). Few studies focused on understanding the association of health literacy to surgical outcomes (12%, n = 6) and even fewer studies developed interventions to address health literacy (4%, n = 2).
DISCUSSION
Low health literacy is common among surgical patients. Important opportunities exist to better understand the role of health literacy in determining surgical outcomes and to develop more health literacy-sensitive models of surgical care. [HLRP: Health Literacy Research and Practice. 2020;4(1):e45-e65.] PLAIN LANGUAGE SUMMARY: Health literacy has not been well-studied in surgery but likely plays an important role. In this article, we reviewed all current research on health literacy in surgery to help us understand where we are at and where we need to go. We found that low health literacy is common and we need more ways to address it in surgery.
Topics: Health Literacy; Humans; Surgical Procedures, Operative
PubMed: 32053207
DOI: 10.3928/24748307-20191121-01 -
Preventing Chronic Disease Nov 2017Physical activity (PA) is strongly endorsed for managing chronic conditions, and a vital sign tool (indicator of general physical condition) could alert providers of... (Review)
Review
INTRODUCTION
Physical activity (PA) is strongly endorsed for managing chronic conditions, and a vital sign tool (indicator of general physical condition) could alert providers of inadequate PA to prompt counseling or referral. This systematic review examined the use, definitions, psychometric properties, and outcomes of brief PA instruments as vital sign measures, with attention primarily to studies focused on arthritis.
METHODS
Electronic databases were searched for English-language literature from 1985 through 2016 using the terms PA, exercise, vital sign, exercise referral scheme, and exercise counseling. Of the 838 articles identified for title and abstract review, 9 articles qualified for full text review and data extraction.
RESULTS
Five brief PA measures were identified: Exercise Vital Sign (EVS), Physical Activity Vital Sign (PAVS), Speedy Nutrition and Physical Activity Assessment (SNAP), General Practice Physical Activity Questionnaire (GPPAQ), and Stanford Brief Activity Survey (SBAS). Studies focusing on arthritis were not found. Over 1.5 years of using EVS in a large hospital system, improvements occurred in relative weight loss among overweight patients and reduction in glycosylated hemoglobin among diabetic patients. On PAVS, moderate physical activity of 5 or more days per week versus fewer than 5 days per week was associated with a lower body mass index (-2.90 kg/m). Compared with accelerometer-defined physical activity, EVS was weakly correlated (r = 0.27), had low sensitivity (27%-59%), and high specificity (74%-89%); SNAP showed weak agreement (κ = 0.12); GPPAQ had moderate sensitivity (46%) and specificity (50%), and SBAS was weakly correlated (r = 0.10-0.28), had poor to moderate sensitivity (18%-67%), and had moderate specificity (58%-79%).
CONCLUSION
Few studies have examined a brief physical activity tool as a vital sign measure. Initial investigations suggest the promise of these simple and quick assessment tools, and research is needed to test the effects of their use on chronic disease outcomes.
Topics: Exercise; Health Behavior; Humans; Vital Signs
PubMed: 29191260
DOI: 10.5888/pcd14.170030 -
Frontiers in Psychology 2022The objective of this article was to review existing research to assess the evidence for predictive processing (PP) in sign language, the conditions under which it...
UNLABELLED
The objective of this article was to review existing research to assess the evidence for predictive processing (PP) in sign language, the conditions under which it occurs, and the effects of language mastery (sign language as a first language, sign language as a second language, bimodal bilingualism) on the neural bases of PP. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. We searched peer-reviewed electronic databases (SCOPUS, Web of Science, PubMed, ScienceDirect, and EBSCO host) and gray literature (dissertations in ProQuest). We also searched the reference lists of records selected for the review and forward citations to identify all relevant publications. We searched for records based on five criteria (original work, peer-reviewed, published in English, research topic related to PP or neural entrainment, and human sign language processing). To reduce the risk of bias, the remaining two authors with expertise in sign language processing and a variety of research methods reviewed the results. Disagreements were resolved through extensive discussion. In the final review, 7 records were included, of which 5 were published articles and 2 were dissertations. The reviewed records provide evidence for PP in signing populations, although the underlying mechanism in the visual modality is not clear. The reviewed studies addressed the motor simulation proposals, neural basis of PP, as well as the development of PP. All studies used dynamic sign stimuli. Most of the studies focused on semantic prediction. The question of the mechanism for the interaction between one's sign language competence (L1 vs. L2 vs. bimodal bilingual) and PP in the manual-visual modality remains unclear, primarily due to the scarcity of participants with varying degrees of language dominance. There is a paucity of evidence for PP in sign languages, especially for frequency-based, phonetic (articulatory), and syntactic prediction. However, studies published to date indicate that Deaf native/native-like L1 signers predict linguistic information during sign language processing, suggesting that PP is an amodal property of language processing.
SYSTEMATIC REVIEW REGISTRATION
[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021238911], identifier [CRD42021238911].
PubMed: 35496220
DOI: 10.3389/fpsyg.2022.805792