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BMJ Open Oct 2020Thrombocytopaenia is one of the most common haemostatic abnormalities among neonates. It affects approximately one-quarter of neonates admitted into neonatal intensive...
INTRODUCTION
Thrombocytopaenia is one of the most common haemostatic abnormalities among neonates. It affects approximately one-quarter of neonates admitted into neonatal intensive care units and may lead to a high risk of bleeding and mortality, which are substantial causes for concern by neonatologists. Platelet transfusion (PT) is a specific treatment for thrombocytopaenia. To date, PT thresholds are diverse since the associations between low platelet count and negative outcomes are not clear. We propose this protocol for a systematic review to collect and assess evidence concerning the best PT threshold to reduce mortality, bleeding and major morbidity among neonates with thrombocytopaenia.
METHODS AND ANALYSIS
The systematic review will be performed according to the Cochrane Handbook for Systematic Review of Interventions, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement, and the Grading of Recommendations Assessment, Development and Evaluation system. Two independent researchers will perform the study selection, data extraction/coding, quality assessment and further analyses of the included studies, with disagreements being resolved by a third researcher. A systematic search of the literature will be conducted in the PubMed, Cochrane Library and Embase databases from database inception through 13 October 2020. All randomised controlled trials, cohort studies and case-control studies will be included without any restrictions regarding publication date or language. The primary outcomes will comprise in-hospital mortality and bleeding episodes. Endnote X9 and Review Manager V.5.3 software will be used to manage the selection process and statistical analysis, respectively. If the included studies are sufficient and homogeneous for any of the outcomes, a quantitative synthesis (meta-analysis) may be performed. Otherwise, we will conduct a narrative systematic review of the results.
ETHICS AND DISSEMINATION
Ethical approval is not required for this study because the data will be obtained from published studies and will not include individual patient data. The results of this study are anticipated to be published in a peer-reviewed journal.
PROSPERO REGISTRATION NUMBER
CRD42020169262.
Topics: Case-Control Studies; Hospital Mortality; Humans; Infant, Newborn; Meta-Analysis as Topic; Platelet Transfusion; Research Design; Thrombocytopenia
PubMed: 33067290
DOI: 10.1136/bmjopen-2020-039132 -
PloS One 2022In this study, we statistically identified and characterized the relationship between the long-run social benefits of creativity and the in-life individual costs (in...
In this study, we statistically identified and characterized the relationship between the long-run social benefits of creativity and the in-life individual costs (in terms of happiness and health) of creativity. To do so, we referred to a theoretical framework that depicts a creator's life. We generated a balanced dataset of 200 creators (i.e., composers, painters, mathematicians and physicists, and biologists and chemists born between 1770 and 1879), and calculated standardized evaluations of the long-run social benefits in different domains (performances, exhibitions, citations). We performed regression analysis and identified the statistical determinants of the relationship between a creator's social benefits and the costs to their happiness and health. We found that creativity represented an individual cost for all four creator groups, with a larger impact on happiness than on health; the cost was greater if creativity was based more on divergent than on convergent thinking or if authors faced greater language issues. The impacts of long-run social benefits on individual happiness and health were similar in the arts and sciences if institutional differences were taken into account.
Topics: Creativity; Happiness; Health Personnel; Humans; Research Design
PubMed: 35476792
DOI: 10.1371/journal.pone.0265446 -
BMC Medical Research Methodology Nov 2022Diagnostic evidence of the accuracy of a test for identifying a target condition of interest can be estimated using systematic approaches following standardized...
BACKGROUND
Diagnostic evidence of the accuracy of a test for identifying a target condition of interest can be estimated using systematic approaches following standardized methodologies. Statistical methods for the meta-analysis of diagnostic test accuracy (DTA) studies are relatively complex, presenting a challenge for reviewers without extensive statistical expertise. In 2006, we developed Meta-DiSc, a free user-friendly software to perform test accuracy meta-analysis. This statistical program is now widely used for performing DTA meta-analyses. We aimed to build a new version of the Meta-DiSc software to include statistical methods based on hierarchical models and an enhanced web-based interface to improve user experience.
RESULTS
In this article, we present the updated version, Meta-DiSc 2.0, a web-based application developed using the R Shiny package. This new version implements recommended state-of-the-art statistical models to overcome the limitations of the statistical approaches included in the previous version. Meta-DiSc 2.0 performs statistical analyses of DTA reviews using a bivariate random effects model. The application offers a thorough analysis of heterogeneity, calculating logit variance estimates of sensitivity and specificity, the bivariate I-squared, the area of the 95% prediction ellipse, and the median odds ratios for sensitivity and specificity, and facilitating subgroup and meta-regression analyses. Furthermore, univariate random effects models can be applied to meta-analyses with few studies or with non-convergent bivariate models. The application interface has an intuitive design set out in four main menus: file upload; graphical description (forest and ROC plane plots); meta-analysis (pooling of sensitivity and specificity, estimation of likelihood ratios and diagnostic odds ratio, sROC curve); and summary of findings (impact of test through downstream consequences in a hypothetical population with a given prevalence). All computational algorithms have been validated in several real datasets by comparing results obtained with STATA/SAS and MetaDTA packages.
CONCLUSION
We have developed and validated an updated version of the Meta-DiSc software that is more accessible and statistically sound. The web application is freely available at www.metadisc.es .
Topics: Humans; Algorithms; Diagnostic Tests, Routine; Odds Ratio; Records; Software; Meta-Analysis as Topic
PubMed: 36443653
DOI: 10.1186/s12874-022-01788-2 -
Dental Materials : Official Publication... Sep 2023The present in vitro study aimed to evaluate the accuracy of three-dimensional (3D) printed indirect bonding trays consisting of hard or soft resin materials produced...
OBJECTIVES
The present in vitro study aimed to evaluate the accuracy of three-dimensional (3D) printed indirect bonding trays consisting of hard or soft resin materials produced using computer-aided design and manufacturing (CAD/CAM).
METHODS
Forty-eight dental casts were 3D printed. Four groups based on frontal crowding were defined and divided into hard- and soft-resin groups. After virtual bracket positioning on the digital models, the transfer trays were 3D printed. To evaluate the accuracy of the procedure, measurements were performed using a digital overlay of the virtual (target) bracket position and a post-bonding scan. The horizontal, transverse, and vertical deviations and angular discrepancies were analyzed. The loss rate was evaluated descriptively as a percentage.
RESULTS
A total of 553 brackets were bonded using 24 soft and 24 resilient indirect bonding trays. The mean deviations were of 0.05 mm (transversal), 0.05 mm (horizontal), 0.09 mm (vertical), 0.13° (angulation) in the resilient resin group and of 0.01 mm (transversal), 0.08 mm (horizontal), 0.08 mm (vertical), 0.37° (angular) in the soft resin group. The loss rate was 6.9% and 0.7% in the hard and soft resin groups, respectively. Angular deviations were significantly higher in the soft resin group (P = 0.009), whereas the loss rate was considerably higher in the hard resin group (P < 0.001).
SIGNIFICANCE
The findings indicate that indirect bonding using CAD/CAM is an accurate procedure in the laboratory setting. Soft resins are considered favorable for loss rate and useability.
Topics: Computer-Aided Design; Dental Bonding; Models, Dental; Orthodontic Brackets; Research Design; Single-Blind Method
PubMed: 37482433
DOI: 10.1016/j.dental.2023.07.003 -
BMC Bioinformatics Oct 2022Recently, Deep Learning based automatic generation of treatment recommendation has been attracting much attention. However, medical datasets are usually small, which may...
Recently, Deep Learning based automatic generation of treatment recommendation has been attracting much attention. However, medical datasets are usually small, which may lead to over-fitting and inferior performances of deep learning models. In this paper, we propose multi-objective data enhancement method to indirectly scale up the medical data to avoid over-fitting and generate high quantity treatment recommendations. Specifically, we define a main and several auxiliary tasks on the same dataset and train a specific model for each of these tasks to learn different aspects of knowledge in limited data scale. Meanwhile, a Soft Parameter Sharing method is exploited to share learned knowledge among models. By sharing the knowledge learned by auxiliary tasks to the main task, the proposed method can take different semantic distributions into account during the training process of the main task. We collected an ultrasound dataset of thyroid nodules that contains Findings, Impressions and Treatment Recommendations labeled by professional doctors. We conducted various experiments on the dataset to validate the proposed method and justified its better performance than existing methods.
Topics: Neural Networks, Computer; Deep Learning; Research Design; Knowledge
PubMed: 36266626
DOI: 10.1186/s12859-022-04985-4 -
Population Studies Nov 2021Testing theories about human senescence and longevity demands accurate information on older-adult mortality; this is rare in low- to middle-income countries where raw...
Testing theories about human senescence and longevity demands accurate information on older-adult mortality; this is rare in low- to middle-income countries where raw data may be distorted by defective completeness and systematic age misreporting. For this reason, such populations are frequently excluded from empirical tests of mortality and longevity theories, thus limiting their reach, as they reflect only a small and selected human mortality experience. In this paper we formulate an integrated method to compute estimates of older-adult mortality when vital registration and population counts are defective due to inaccurate coverage and/or systematic age misreporting. The procedure is validated with a simulation study that identifies a strategy to compute adjustments, which, under some assumptions, performs quite well. While the paper focuses on Latin American and Caribbean countries, the method is quite general and, with additional information and some model reformulation, could be applied to other populations with similar problems.
Topics: Adult; Humans; Mortality; Research Design
PubMed: 34002662
DOI: 10.1080/00324728.2021.1918752 -
Clinical Science (London, England :... Jan 2023Existing strategies to identify relevant studies for systematic review may not perform equally well across research domains. We compare four approaches based on either...
OBJECTIVE
Existing strategies to identify relevant studies for systematic review may not perform equally well across research domains. We compare four approaches based on either human or automated screening of either title and abstract or full text, and report the training of a machine learning algorithm to identify in vitro studies from bibliographic records.
METHODS
We used a systematic review of oxygen-glucose deprivation (OGD) in PC-12 cells to compare approaches. For human screening, two reviewers independently screened studies based on title and abstract or full text, with disagreements reconciled by a third. For automated screening, we applied text mining to either title and abstract or full text. We trained a machine learning algorithm with decisions from 2000 randomly selected PubMed Central records enriched with a dataset of known in vitro studies.
RESULTS
Full-text approaches performed best, with human (sensitivity: 0.990, specificity: 1.000 and precision: 0.994) outperforming text mining (sensitivity: 0.972, specificity: 0.980 and precision: 0.764). For title and abstract, text mining (sensitivity: 0.890, specificity: 0.995 and precision: 0.922) outperformed human screening (sensitivity: 0.862, specificity: 0.998 and precision: 0.975). At our target sensitivity of 95% the algorithm performed with specificity of 0.850 and precision of 0.700.
CONCLUSION
In this in vitro systematic review, human screening based on title and abstract erroneously excluded 14% of relevant studies, perhaps because title and abstract provide an incomplete description of methods used. Our algorithm might be used as a first selection phase in in vitro systematic reviews to limit the extent of full text screening required.
Topics: Humans; Data Mining; Algorithms; Research Design; Machine Learning; Glucose
PubMed: 36630537
DOI: 10.1042/CS20220594 -
BMC Bioinformatics Jun 2022Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use...
BACKGROUND
Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Our model performs variable selection through spike-and-slab priors that borrow information across clustered data. We use this model to predict overall patient survival from the Cancer Genome Atlas with data from 29 cancer types and 4 omics sources and use simulations to characterize the performance of the hierarchical spike-and-slab prior.
RESULTS
We found that molecular patterns shared across all or most cancers were largely not predictive of survival. However, our model selected patterns unique to subsets of cancers that differentiate clinical tumor subtypes with markedly different survival outcomes. Some of these subtypes were previously established, such as subtypes of uterine corpus endometrial carcinoma, while others may be novel, such as subtypes within a set of kidney carcinomas. Through simulations, we found that the hierarchical spike-and-slab prior performs best in terms of variable selection accuracy and predictive power when borrowing information is advantageous, but also offers competitive performance when it is not.
CONCLUSIONS
We address the issue of prediction across multiple sources of data by using results from BIDIFAC+ in a Bayesian hierarchical model for overall patient survival. By incorporating spike-and-slab priors that borrow information across cancers, we identified molecular patterns that distinguish clinical tumor subtypes within a single cancer and within a group of cancers. We also corroborate the flexibility and performance of using spike-and-slab priors as a Bayesian variable selection approach.
Topics: Bayes Theorem; Carcinoma, Renal Cell; Humans; Kidney Neoplasms; Research Design
PubMed: 35710340
DOI: 10.1186/s12859-022-04770-3 -
PloS One 2021Identifying barriers and facilitators in HIV-indicator reporting contributes to strengthening HIV monitoring and evaluation efforts by acknowledging contributors to...
Identifying barriers and facilitators in HIV-indicator reporting contributes to strengthening HIV monitoring and evaluation efforts by acknowledging contributors to success, as well as identifying weaknesses within the system that require improvement. Nonetheless, there is paucity in identifying and comparing barriers and facilitators in HIV-indicator data reporting among facilities that perform well and those that perform poorly at meeting reporting completeness and timeliness requirements. Therefore, this study aims to use a qualitative approach in identifying and comparing the current state of barriers and facilitators in routine reporting of HIV-indicators by facilities performing well, and those performing poorly in meeting facility reporting completeness and timeliness requirements to District Health Information Software2 (DHIS2). A multiple qualitative case study design was employed. The criteria for case selection was based on performance in HIV-indicator facility reporting completeness and timeliness. Areas of interest revolved around reporting procedures, organizational, behavioral, and technical factors. Purposive sampling was used to identify key informants in the study. Data was collected using semi-structured in-depth interviews with 13 participants, and included archival records on facility reporting performance, looking into documentation, and informal direct observation at 13 facilities in Kenya. Findings revealed that facilitators and barriers in reporting emerged from the following factors: interrelationship between workload, teamwork and skilled personnel, role of an EMRs system in reporting, time constraints, availability and access-rights to DHIS2, complexity of reports, staff rotation, availability of trainings and mentorship, motivation, availability of standard operating procedures and resources. There was less variation in barriers and facilitators faced by facilities performing well and those performing poorly. Continuous evaluations have been advocated within health information systems literature. Therefore, continuous qualitative assessments are also necessary in order to determine improvements and recurring of similar issues. These assessments have also complemented other quantitative analyses related to this study.
Topics: Electronic Health Records; HIV Infections; Health Facilities; Health Information Systems; Humans; Kenya; Qualitative Research; Research Design
PubMed: 33630971
DOI: 10.1371/journal.pone.0247525 -
The Lancet. Global Health Jun 2023Primary care is of insufficient quality in many low-income and middle-income countries. Some health facilities perform better than others despite operating in similar...
BACKGROUND
Primary care is of insufficient quality in many low-income and middle-income countries. Some health facilities perform better than others despite operating in similar contexts, although the factors that characterise best performance are not well known. Existing best-performance analyses are concentrated in high-income countries and focus on hospitals. We used the positive deviance approach to identify the factors that differentiate best from worst primary care performance among health facilities across six low-resource health systems.
METHODS
This positive deviance analysis used nationally representative samples of public and private health facilities from Service Provision Assessments of the Democratic Republic of the Congo, Haiti, Malawi, Nepal, Senegal, and Tanzania. Data were collected starting June 11, 2013, in Malawi and ending Feb 28, 2020, in Senegal. We assessed facility performance through completion of the Good Medical Practice Index (GMPI) of essential clinical actions (eg, taking a thorough history, conducting an adequate physical examination) according to clinical guidelines and measured with direct observations of care. We identified hospitals and clinics in the top decile of performance (defined as best performers) and conducted a quantitative, cross-national positive deviance analysis to compare them with facilities performing below the median (defined as worst performers) and identify facility-level factors that explain the gap between best and worst performance.
FINDINGS
We identified 132 best-performing and 664 worst-performing hospitals, and 355 best-performing and 1778 worst-performing clinics based on clinical performance across countries. The mean GMPI score was 0·81 (SD 0·07) for the best-performing hospitals and 0·44 (0·09) for the worst-performing hospitals. Among clinics, mean GMPI scores were 0·75 (0·07) for the best performers and 0·34 (0·10) for the worst performers. High-quality governance, management, and community engagement were associated with best performance compared with worst performance. Private facilities out-performed government-owned hospitals and clinics.
INTERPRETATION
Our findings suggest that best-performing health facilities are characterised by good management and leaders who can engage staff and community members. Governments should look to best performers to identify scalable practices and conditions for success that can improve primary care quality overall and decrease quality gaps between health facilities.
FUNDING
Bill & Melinda Gates Foundation.
Topics: Humans; Developing Countries; Health Services; Quality of Health Care; Health Facilities; Malawi
PubMed: 37202022
DOI: 10.1016/S2214-109X(23)00163-8