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BMC Musculoskeletal Disorders Jun 2024Taping is increasingly used to manage proprioceptive deficits, but existing reviews on its impact have shortcomings. To accurately assess the effects of taping, a... (Meta-Analysis)
Meta-Analysis
Taping is increasingly used to manage proprioceptive deficits, but existing reviews on its impact have shortcomings. To accurately assess the effects of taping, a separate meta-analyses for different population groups and tape types is needed. Therefore, both between- and within-group meta-analyses are needed to evaluate the influence of taping on proprioception. According to PRISMA guidelines, a literature search was conducted across seven databases (Web of Science, PEDro, Pubmed, EBSCO, Scopus, ERIC, SportDiscus, Psychinfo) and one register (CENTRAL) using the keywords "tape" and "proprioception". Out of 1372 records, 91 studies, involving 2718 individuals, met the inclusion criteria outlined in the systematic review. The meta-analyses revealed a significant between and within-group reduction in repositioning errors with taping compared to no tape (Hedge's g: -0.39, p < 0.001) and placebo taping (Hedge's g: -1.20, p < 0.001). Subgroup and sensitivity analyses further confirmed the reliability of the overall between and within-group analyses. The between-group results further demonstrated that both elastic tape and rigid tape had similar efficacy to improve repositioning errors in both healthy and fatigued populations. Additional analyses on the threshold to detection of passive motion and active movement extent discrimination apparatus revealed no significant influence of taping. In conclusion, the findings highlight the potential of taping to enhance joint repositioning accuracy compared to no tape or placebo taping. Further research needs to uncover underlying mechanisms and refine the application of taping for diverse populations with proprioceptive deficits.
Topics: Humans; Proprioception; Athletic Tape
PubMed: 38890668
DOI: 10.1186/s12891-024-07571-2 -
Current Hypertension Reports Jul 2024Machine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthesise the literature on ML and classical regression studies... (Review)
Review
PURPOSE OF REVIEW
Machine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthesise the literature on ML and classical regression studies exploring potential prognostic factors and to compare prediction performance for pre-eclampsia.
RECENT FINDINGS
From 9382 studies retrieved, 82 were included. Sixty-six publications exclusively reported eighty-four classical regression models to predict variable timing of onset of pre-eclampsia. Another six publications reported purely ML algorithms, whilst another 10 publications reported ML algorithms and classical regression models in the same sample with 8 of 10 findings that ML algorithms outperformed classical regression models. The most frequent prognostic factors were age, pre-pregnancy body mass index, chronic medical conditions, parity, prior history of pre-eclampsia, mean arterial pressure, uterine artery pulsatility index, placental growth factor, and pregnancy-associated plasma protein A. Top performing ML algorithms were random forest (area under the curve (AUC) = 0.94, 95% confidence interval (CI) 0.91-0.96) and extreme gradient boosting (AUC = 0.92, 95% CI 0.90-0.94). The competing risk model had similar performance (AUC = 0.92, 95% CI 0.91-0.92) compared with a neural network. Calibration performance was not reported in the majority of publications. ML algorithms had better performance compared to classical regression models in pre-eclampsia prediction. Random forest and boosting-type algorithms had the best prediction performance. Further research should focus on comparing ML algorithms to classical regression models using the same samples and evaluation metrics to gain insight into their performance. External validation of ML algorithms is warranted to gain insights into their generalisability.
Topics: Humans; Pre-Eclampsia; Pregnancy; Female; Machine Learning; Algorithms; Prognosis; Regression Analysis; Risk Assessment; Risk Factors; Predictive Value of Tests
PubMed: 38806766
DOI: 10.1007/s11906-024-01297-1 -
Radiotherapy and Oncology : Journal of... Jul 2024We performed this systematic review and meta-analysis to investigate the performance of ML in detecting genetic mutation status in NSCLC patients. (Meta-Analysis)
Meta-Analysis Review
BACKGROUND AND PURPOSE
We performed this systematic review and meta-analysis to investigate the performance of ML in detecting genetic mutation status in NSCLC patients.
MATERIALS AND METHODS
We conducted a systematic search of PubMed, Cochrane, Embase, and Web of Science up until July 2023. We discussed the genetic mutation status of EGFR, ALK, KRAS, and BRAF, as well as the mutation status at different sites of EGFR.
RESULTS
We included a total of 128 original studies, of which 114 constructed ML models based on radiomic features mainly extracted from CT, MRI, and PET-CT data. From a genetic mutation perspective, 121 studies focused on EGFR mutation status analysis. In the validation set, for the detection of EGFR mutation status, the aggregated c-index was 0.760 (95%CI: 0.706-0.814) for clinical feature-based models, 0.772 (95%CI: 0.753-0.791) for CT-based radiomics models, 0.816 (95%CI: 0.776-0.856) for MRI-based radiomics models, and 0.750 (95%CI: 0.712-0.789) for PET-CT-based radiomics models. When combined with clinical features, the aggregated c-index was 0.807 (95%CI: 0.781-0.832) for CT-based radiomics models, 0.806 (95%CI: 0.773-0.839) for MRI-based radiomics models, and 0.822 (95%CI: 0.789-0.854) for PET-CT-based radiomics models. In the validation set, the aggregated c-indexes for radiomics-based models to detect mutation status of ALK and KRAS, as well as the mutation status at different sites of EGFR were all greater than 0.7.
CONCLUSION
The use of radiomics-based methods for early discrimination of EGFR mutation status in NSCLC demonstrates relatively high accuracy. However, the influence of clinical variables cannot be overlooked in this process. In addition, future studies should also pay attention to the accuracy of radiomics in identifying mutation status of other genes in EGFR.
Topics: Humans; Lung Neoplasms; Machine Learning; Mutation; Carcinoma, Non-Small-Cell Lung; Positron Emission Tomography Computed Tomography; ErbB Receptors; Proto-Oncogene Proteins p21(ras)
PubMed: 38734145
DOI: 10.1016/j.radonc.2024.110325 -
Translational Vision Science &... Apr 2024The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing cataract surgery videos.
PURPOSE
The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing cataract surgery videos.
METHODS
A systematic review of the literature about intra-operative analysis of cataract surgery videos with machine learning techniques was performed. Cataract diagnosis and detection algorithms were excluded. Resulting algorithms were compared, descriptively analyzed, and metrics summarized or visually reported. The reproducibility and reliability of the methods and results were assessed using a modified version of the Medical Image Computing and Computer-Assisted (MICCAI) checklist.
RESULTS
Thirty-eight of the 550 screened studies were included, 20 addressed the challenge of instrument detection or tracking, 9 focused on phase discrimination, and 8 predicted skill and complications. Instrument detection achieves an area under the receiver operator characteristic curve (ROC AUC) between 0.976 and 0.998, instrument tracking an mAP between 0.685 and 0.929, phase recognition an ROC AUC between 0.773 and 0.990, and complications or surgical skill performs with an ROC AUC between 0.570 and 0.970.
CONCLUSIONS
The studies showed a wide variation in quality and pose a challenge regarding replication due to a small number of public datasets (none for manual small incision cataract surgery) and seldom published source code. There is no standard for reported outcome metrics and validation of the models on external datasets is rare making comparisons difficult. The data suggests that tracking of instruments and phase detection work well but surgical skill and complication recognition remains a challenge for deep learning.
TRANSLATIONAL RELEVANCE
This overview of cataract surgery analysis with AI models provides translational value for improving training of the clinician by identifying successes and challenges.
Topics: Humans; Artificial Intelligence; Reproducibility of Results; Algorithms; Software; Cataract
PubMed: 38618893
DOI: 10.1167/tvst.13.4.20 -
Heart, Lung & Circulation Apr 2024Risk adjustment following percutaneous coronary intervention (PCI) is vital for clinical quality registries, performance monitoring, and clinical decision-making. There... (Review)
Review
BACKGROUND AND AIM
Risk adjustment following percutaneous coronary intervention (PCI) is vital for clinical quality registries, performance monitoring, and clinical decision-making. There remains significant variation in the accuracy and nature of risk adjustment models utilised in international PCI registries/databases. Therefore, the current systematic review aims to summarise preoperative variables associated with 30-day mortality among patients undergoing PCI, and the other methodologies used in risk adjustments.
METHOD
The MEDLINE, EMBASE, CINAHL, and Web of Science databases until October 2022 without any language restriction were systematically searched to identify preoperative independent variables related to 30-day mortality following PCI. Information was systematically summarised in a descriptive manner following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. The quality and risk of bias of all included articles were assessed using the Prediction Model Risk Of Bias Assessment Tool. Two independent investigators took part in screening and quality assessment.
RESULTS
The search yielded 2,941 studies, of which 42 articles were included in the final assessment. Logistic regression, Cox-proportional hazard model, and machine learning were utilised by 27 (64.3%), 14 (33.3%), and one (2.4%) article, respectively. A total of 74 independent preoperative variables were identified that were significantly associated with 30-day mortality following PCI. Variables that repeatedly used in various models were, but not limited to, age (n=36, 85.7%), renal disease (n=29, 69.0%), diabetes mellitus (n=17, 40.5%), cardiogenic shock (n=14, 33.3%), gender (n=14, 33.3%), ejection fraction (n=13, 30.9%), acute coronary syndrome (n=12, 28.6%), and heart failure (n=10, 23.8%). Nine (9; 21.4%) studies used missing values imputation, and 15 (35.7%) articles reported the model's performance (discrimination) with values ranging from 0.501 (95% confidence interval [CI] 0.472-0.530) to 0.928 (95% CI 0.900-0.956), and four studies (9.5%) validated the model on external/out-of-sample data.
CONCLUSIONS
Risk adjustment models need further improvement in their quality through the inclusion of a parsimonious set of clinically relevant variables, appropriately handling missing values and model validation, and utilising machine learning methods.
PubMed: 38570260
DOI: 10.1016/j.hlc.2024.01.021 -
The Journal of Allergy and Clinical... May 2024Access to the molecular culprits of allergic reactions allows for the leveraging of molecular allergology as a new precision medicine approach-one built on... (Review)
Review
Access to the molecular culprits of allergic reactions allows for the leveraging of molecular allergology as a new precision medicine approach-one built on interdisciplinary, basic, and clinical knowledge. Molecular allergology relies on the use of allergen molecules as tools for the diagnosis and management of allergic patients. It complements the conventional approach based on skin and allergen extract testing. Major applications of molecular allergology comprise accurate identification of the offending allergen thanks to discrimination between genuine sensitization and allergen cross-reactivity, evaluation of potential severity, patient-tailored choice of the adequate allergen immunotherapy, and prediction of its expected efficacy and safety. Allergen cross-reactivity, defined as the recognition of 2 or more allergen molecules by antibodies or T cells of the same specificity, frequently interferes with allergen extract testing. At the mechanistic level, allergen cross-reactivity depends on the allergen, the host's immune response, and the context of their interaction. The multiplicity of allergen molecules and families adds further difficulty. Understanding allergen cross-reactivity at the immunologic level and translating it into a daily tool for the management of allergic patients is further complicated by the ever-increasing number of characterized allergenic molecules, the lack of dedicated resources, and the need for a personalized, patient-centered approach. Conversely, knowledge sharing paves the way for improved clinical use, innovative diagnostic tools, and further interdisciplinary research. Here, we aimed to provide a comprehensive and unbiased state-of-the art systematic review on allergen cross-reactivity. To optimize learning, we enhanced the review with basic, translational, and clinical definitions, clinical vignettes, and an overview of online allergen databases.
PubMed: 38524786
DOI: 10.1016/j.jacig.2024.100230 -
BMC Psychiatry Mar 2024Lived experience workforces are one of the fastest growing emerging disciplines in Australian mental health service settings. Individuals with lived and living...
BACKGROUND
Lived experience workforces are one of the fastest growing emerging disciplines in Australian mental health service settings. Individuals with lived and living experience of mental distress employed in mental health services, often referred to as peer or lived experience workers, are widely considered essential for mental health recovery and reform. Despite vast growth of this workforce, concerns remain over the widespread integration of peer workforces to align with recommended movement of healthcare services toward greater recovery-orientated and person-centered practices. Previous research has identified barriers for peer work integration including a lack of clear role definition, inadequate training, and poor supportive organisational culture. Stigma, discrimination and a lack of acceptance by colleagues are also common themes. This systematic review seeks to identify organisational actions to support integration of peer workforces for improved mental health service delivery.
METHOD
A systematic search was conducted through online databases (n = 8) between January 1980 to November 2023. Additional data were sourced from conference proceedings, hand searching grey literature and scanning reference lists. Qualitative data was extracted and synthesised utilising narrative synthesis to identify key themes and findings reported adhere to PRISMA guidelines. The review protocol was registered with Prospero (CRD: 42,021,257,013).
RESULTS
Four key actions were identified: education and training, organisational readiness, Structural adjustments, resourcing and support and, demonstrated commitment to peer integration and recovery practice.
CONCLUSIONS
The study identifies actions for mental health service organisations and system leaders to adopt in support of integrating peer and lived experience workforces in service delivery.
Topics: Humans; Australia; Mental Disorders; Mental Health; Mental Health Services; Workforce
PubMed: 38500086
DOI: 10.1186/s12888-024-05664-9 -
BMC Medical Education Feb 2024Although the number of older patients requiring medical care is increasing, caring for older patients is often seen as unattractive by medical trainees (i.e., medical...
BACKGROUND
Although the number of older patients requiring medical care is increasing, caring for older patients is often seen as unattractive by medical trainees (i.e., medical students, residents, interns, and fellows). Terror Management Theory states that people have a negative attitude towards older people, because they remind people of their own mortality. We hypothesize that ageism, death anxiety, and ageing anxiety among medical trainees negatively affect their attitude towards medical care for older patients. This review aimed to examine and generate an overview of available literature on the relationship between ageism, death anxiety, and ageing anxiety among medical trainees and their attitude towards medical care for older patients.
METHODS
A systematic review was performed with a review protocol based on the PRISMA Statement. PubMed, Ebsco/PsycInfo, Ebsco/ERIC and Embase were searched from inception to August 2022, using the following search terms, including their synonyms and closely related words: "medical trainees" AND "ageism" OR "death anxiety" OR "ageing anxiety" AND "(attitude AND older patient)".
RESULTS
The search yielded 4072 different studies; 12 eligible studies (10 quantitative and 2 qualitative) were identified and synthesized using narrative synthesis. Findings suggest that a positive attitude towards older people was related to a positive attitude towards medical care for older patients among medical students. The available literature on the relationship between death anxiety and/or ageing anxiety and attitude towards medical care for older patients among medical trainees was limited and had a heterogeneity in focus, which hindered comparison of results.
CONCLUSION
Our findings suggest that a positive attitude towards older people in general is related to a positive attitude towards medical care for older patients among medical students. Future research should focus on further exploring underlying mechanisms affecting the attitude towards medical care for older patients among medical trainees.
Topics: Humans; Aged; Ageism; Students, Medical; Aging; Anxiety; Attitude of Health Personnel; Attitude
PubMed: 38413875
DOI: 10.1186/s12909-024-05147-1 -
Medical Education Online Dec 2024People who identify as lesbian, gay, bisexual, transgender, queer/questioning, intersex, and other sexual/gender minorities (LGBTQ+) may experience discrimination when... (Review)
Review
INTRODUCTION
People who identify as lesbian, gay, bisexual, transgender, queer/questioning, intersex, and other sexual/gender minorities (LGBTQ+) may experience discrimination when seeking healthcare. Medical students should be trained in inclusive and affirming care for LGBTQ+ patients. This narrative literature review explores the landscape of interventions and evaluations related to LGBTQ+ health content taught in medical schools in the USA and suggests strategies for further curriculum development.
METHODS
PubMed, ERIC, and Education Research Complete databases were systematically searched for peer-reviewed articles on LGBTQ+ health in medical student education in the USA published between 1 January 2011-6 February 2023. Articles were screened for eligibility and data was abstracted from all eligible articles. Data abstraction included the type of intervention or evaluation, sample population and size, and key outcomes.
RESULTS
One hundred thirty-four articles met inclusion criteria and were reviewed. This includes 6 (4.5%) that evaluate existing curriculum, 77 (57.5%) study the impact of curriculum components and interventions, 36 (26.9%) evaluate student knowledge and learning experiences, and 15 (11.2%) describe the development of broad learning objectives and curriculum. Eight studies identified student knowledge gaps related to gender identity and affirming care and these topics were covered in 34 curriculum interventions.
CONCLUSION
Medical student education is important to address health disparities faced by the LGBTQ+ community, and has been an increasingly studied topic in the USA. A variety of curriculum interventions at single institutions show promise in enhancing student knowledge and training in LGBTQ+ health. Despite this, multiple studies indicate that students report inadequate education on certain topics with limitations in their knowledge and preparedness to care for LGBTQ+ patients, particularly transgender and gender diverse patients. Additional integration of LGBTQ+ curriculum content in areas of perceived deficits could help better prepare future physicians to care for LGBTQ+ patients and populations.
Topics: Humans; Male; Female; United States; Students, Medical; Gender Identity; Sexual and Gender Minorities; Curriculum; Health Education
PubMed: 38359164
DOI: 10.1080/10872981.2024.2312716 -
Journal of Clinical Medicine Jan 2024The aim of this review was to assess the reliability of machine learning (ML) techniques to predict the functional outcome of total hip arthroplasty. The literature... (Review)
Review
The aim of this review was to assess the reliability of machine learning (ML) techniques to predict the functional outcome of total hip arthroplasty. The literature search was performed up to October 2023, using MEDLINE/PubMed, Embase, Web of Science, and NIH Clinical Trials. Level I to IV evidence was included. Seven studies were identified that included 44,121 patients. The time to follow-up varied from 3 months to more than 2 years. Each study employed one to six ML techniques. The best-performing models were for health-related quality of life (HRQoL) outcomes, with an area under the curve (AUC) of more than 84%. In contrast, predicting the outcome of hip-specific measures was less reliable, with an AUC of between 71% to 87%. Random forest and neural networks were generally the best-performing models. Three studies compared the reliability of ML with traditional regression analysis: one found in favour of ML, one was not clear and stated regression closely followed the best-performing ML model, and one showed a similar AUC for HRQoL outcomes but did show a greater reliability for ML to predict a clinically significant change in the hip-specific function. ML offers acceptable-to-excellent discrimination of predicting functional outcomes and may have a marginal advantage over traditional regression analysis, especially in relation to hip-specific hip functional outcomes.
PubMed: 38276109
DOI: 10.3390/jcm13020603