OBJECTIVE: To investigate and implement semiautomated screening for meta-analyses (MA) in urology under consideration of class imbalance.
METHODS: Machine learning algorithms were trained on data from three MA with detailed information of the screening process. Different methods to account for class imbalance (Sampling (up- and downsampling, weighting and cost-sensitive learning), thresholding) were implemented in different machine learning (ML) algorithms (Random Forest, Logistic Regression with Elastic Net Regularization, Support Vector Machines). Models were optimized for sensitivity. Besides metrics such as specificity, receiver operating curves, total missed studies, and work saved over sampling were calculated.
RESULTS: During training, models trained after downsampling achieved the best results consistently among all algorithms. Computing time ranged between 251 and 5834 s. However, when evaluated on the final test data set, the weighting approach performed best. In addition, thresholding helped to improve results as compared to the standard of 0.5. However, due to heterogeneity of results no clear recommendation can be made for a universal sample size. Misses of relevant studies were 0 for the optimized models except for one review.
CONCLUSIONS: It will be necessary to design a holistic methodology that implements the presented methods in a practical manner, but also takes into account other algorithms and the most sophisticated methods for text preprocessing. In addition, the different methods of a cost-sensitive learning approach can be the subject of further investigations.

Software that employs screening prioritization through active learning (AL) has accelerated the screening process significantly by ranking an unordered set of records by their predicted relevance. However, failing to find a relevant paper might alter the findings of a systematic review, highlighting the importance of identifying elusive papers. The time to discovery (TD) measures how many records are needed to be screened to find a relevant paper, making it a helpful tool for detecting such papers. The main aim of this project was to investigate how the choice of the model and prior knowledge influence the TD values of the hard-to-find relevant papers and their rank orders. A simulation study was conducted, mimicking the screening process on a dataset containing titles, abstracts, and labels used for an already published systematic review. The results demonstrated that AL model choice, and mostly the choice of the feature extractor but not the choice of prior knowledge, significantly influenced the TD values and the rank order of the elusive relevant papers. Future research should examine the characteristics of elusive relevant papers to discover why they might take a long time to be found.

OBJECTIVE: Although comprehensive and widespread guidelines on how to conduct systematic reviews of outcome measurement instruments (OMIs) exist, for example from the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) initiative, key information is often missing in published reports. This article describes the development of an extension of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline: PRISMA-COSMIN for OMIs 2024.
METHODS: The development process followed the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines and included a literature search, expert consultations, a Delphi study, a hybrid workgroup meeting, pilot testing, and an end-of-project meeting, with integrated patient/public involvement.
RESULTS: From the literature and expert consultation, 49 potentially relevant reporting items were identified. Round 1 of the Delphi study was completed by 103 panelists, whereas round 2 and 3 were completed by 78 panelists. After 3 rounds, agreement (≥ 67%) on inclusion and wording was reached for 44 items. Eleven items without consensus for inclusion and/or wording were discussed at a workgroup meeting attended by 24 participants. Agreement was reached for the inclusion and wording of 10 items, and the deletion of 1 item. Pilot testing with 65 authors of OMI systematic reviews further improved the guideline through minor changes in wording and structure, finalized during the end-of-project meeting. The final checklist to facilitate the reporting of full systematic review reports contains 54 (sub)items addressing the review\'s title, abstract, plain language summary, open science, introduction, methods, results, and discussion. Thirteen items pertaining to the title and abstract are also included in a separate abstract checklist, guiding authors in reporting for example conference abstracts.
CONCLUSIONS: PRISMA-COSMIN for OMIs 2024 consists of two checklists (full reports; abstracts), their corresponding explanation and elaboration documents detailing the rationale and examples for each item, and a data flow diagram. PRISMA-COSMIN for OMIs 2024 can improve the reporting of systematic reviews of OMIs, fostering their reproducibility and allowing end-users to appraise the quality of OMIs and select the most appropriate OMI for a specific application. NOTE: In order to encourage its wide dissemination this article is freely accessible on the web sites of the journals: Health and Quality of Life Outcomes; Journal of Clinical Epidemiology; Journal of Patient-Reported Outcomes; Quality of Life Research.

OBJECTIVE: Although comprehensive and widespread guidelines on how to conduct systematic reviews of outcome measurement instruments (OMIs) exist, for example from the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) initiative, key information is often missing in published reports. This article describes the development of an extension of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline: PRISMA-COSMIN for OMIs 2024.
METHODS: The development process followed the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines and included a literature search, expert consultations, a Delphi study, a hybrid workgroup meeting, pilot testing, and an end-of-project meeting, with integrated patient/public involvement.
RESULTS: From the literature and expert consultation, 49 potentially relevant reporting items were identified. Round 1 of the Delphi study was completed by 103 panelists, whereas round 2 and 3 were completed by 78 panelists. After 3 rounds, agreement (≥67%) on inclusion and wording was reached for 44 items. Eleven items without consensus for inclusion and/or wording were discussed at a workgroup meeting attended by 24 participants. Agreement was reached for the inclusion and wording of 10 items, and the deletion of 1 item. Pilot testing with 65 authors of OMI systematic reviews further improved the guideline through minor changes in wording and structure, finalized during the end-of-project meeting. The final checklist to facilitate the reporting of full systematic review reports contains 54 (sub)items addressing the review\'s title, abstract, plain language summary, open science, introduction, methods, results, and discussion. Thirteen items pertaining to the title and abstract are also included in a separate abstract checklist, guiding authors in reporting for example conference abstracts.
CONCLUSIONS: PRISMA-COSMIN for OMIs 2024 consists of two checklists (full reports; abstracts), their corresponding explanation and elaboration documents detailing the rationale and examples for each item, and a data flow diagram. PRISMA-COSMIN for OMIs 2024 can improve the reporting of systematic reviews of OMIs, fostering their reproducibility and allowing end-users to appraise the quality of OMIs and select the most appropriate OMI for a specific application. NOTE: In order to encourage its wide dissemination this article is freely accessible on the web sites of the journals: Health and Quality of Life Outcomes; Journal of Clinical Epidemiology; Journal of Patient-Reported Outcomes; Quality of Life Research.

UNASSIGNED: This systematic review and meta-analysis aims to investigate the effects of virtual reality (VR) exercise compared to traditional rehabilitation on pain, function, and muscle strength in patients with knee osteoarthritis (KOA). Additionally, the study explores the mechanisms by which VR exercise contributes to the rehabilitation of KOA patients.
UNASSIGNED: We systematically searched PubMed, the Cochrane Library, Embase, Web of Science, Scopus, and PEDro according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our search spanned from the library construction to 24 May 2024, focusing on randomized controlled trials Primary outcomes included pain, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and muscle strength. Meta-analysis was conducted using RevMan (version 5.4) and Stata (version 14.0). The bias risk of included studies was assessed using the Cochrane RoB 2.0 tool, while the quality of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach.
UNASSIGNED: This meta-analysis and systematic review included nine studies involving 456 KOA patients. The results indicated that VR exercise significantly improved pain scores (SMD, -1.53; 95% CI: -2.50 to -0.55; p = 0.002), WOMAC total score (MD, -14.79; 95% CI: -28.26 to -1.33; p = 0.03), WOMAC pain score (MD, -0.93; 95% CI: -1.52 to -0.34; p = 0.002), knee extensor strength (SMD, 0.51; 95% CI: 0.14 to 0.87; p = 0.006), and knee flexor strength (SMD, 0.65; 95% CI: 0.28 to 1.01; p = 0.0005), but not significantly for WOMAC stiffness (MD, -0.01; 95% CI: -1.21 to 1.19; p = 0.99) and physical function (MD, -0.35; 95% CI: -0.79 to -0.09; p = 0.12).
UNASSIGNED: VR exercise significantly alleviates pain, enhances muscle strength and WOMAC total score in KOA patients, but improvements in joint stiffness and physical function are not significant. However, the current number of studies is limited, necessitating further research to expand on the present findings.
UNASSIGNED: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024540061, identifier CRD42024540061.

UNASSIGNED: The rehabilitation of central post-stroke pain (CPSP) is a complex clinical challenge, and repetitive transcranial magnetic stimulation (rTMS) has been widely applied in the research of neurofunctional recovery following stroke. However, there is currently no reliable evidence-based medicine supporting the efficacy of rTMS in central post-stroke pain. This review aims to evaluate the effects of rTMS on central post-stroke pain.
UNASSIGNED: Following the PRISMA guidelines, we conducted searches on PubMed, Cochrane Library, Embase, Web of Science, CNKI, and Wan Fang Data Knowledge Service Platform. We searched for randomized controlled trials (RCTs) investigating the use of rTMS in treating central post-stroke pain, and conducted screening based on inclusion and exclusion criteria. Characteristics of the included RCTs were extracted. The heterogeneity of the trials was assessed using the I2 statistic. Meta-analysis was performed using Stata 17 software. Bias risk and methodological quality were evaluated using the Cochrane RoB 2 tool and the Pedro scale.
UNASSIGNED: A total of six randomized controlled trials involving 288 patients met our inclusion criteria. In our analysis, rTMS was more effective in treating patients with CPSP compared to the placebo group (SMD=-1.15, 95% CI: -1.69, -0.61, P < 0.001). Furthermore, results from subgroup analysis indicated no statistically significant difference in the improvement of pain for durations exceeding 6 months when comparing rTMS to conventional treatment (SMD=-0.80, 95% CI: -1.63, 0.03, P = 0.059).
UNASSIGNED: TMS can alleviate pain in CPSP patients and improve their motor function, but its effects on depression, anxiety, and MEP-latency are not significant.
UNASSIGNED: https://www.crd.york.ac.uk/prospero/, CRD42024497530.

BACKGROUND: As evidence related to the COVID-19 pandemic surged, databases, platforms, and repositories evolved with features and functions to assist users in promptly finding the most relevant evidence. In response, research synthesis teams adopted novel searching strategies to sift through the vast amount of evidence to synthesize and disseminate the most up-to-date evidence. This paper explores the key database features that facilitated systematic searching for rapid evidence synthesis during the COVID-19 pandemic to inform knowledge management infrastructure during future global health emergencies.
METHODS: This paper outlines the features and functions of previously existing and newly created evidence sources routinely searched as part of the NCCMT\'s Rapid Evidence Service methods, including databases, platforms, and repositories. Specific functions of each evidence source were assessed as they pertain to searching in the context of a public health emergency, including the topics of indexed citations, the level of evidence of indexed citations, and specific usability features of each evidence source.
RESULTS: Thirteen evidence sources were assessed, of which four were newly created and nine were either pre-existing or adapted from previously existing resources. Evidence sources varied in topics indexed, level of evidence indexed, and specific searching functions.
CONCLUSIONS: This paper offers insights into which features enabled systematic searching for the completion of rapid reviews to inform decision makers within 5-10 days. These findings provide guidance for knowledge management strategies and evidence infrastructures during future public health emergencies.

Evidence-informed decision making is based on the premise that the entirety of information on a topic is collected and analyzed. Systematic reviews allow for data from different studies to be rigorously assessed according to PICO principles (population, intervention, control, outcomes). However, conducting a systematic review is generally a slow process that is a significant drain on resources. The fundamental problem is that the current approach to creating a systematic review cannot scale to meet the challenges resulting from the massive body of unstructured evidence. For this reason, the Public Health Agency of Canada has been examining the automation of different stages of evidence synthesis to increase efficiencies. In this article, we present an overview of an initial version of a novel machine learning-based system that is powered by recent advances in natural language processing (NLP), such as BioBERT, with further optimizations completed using a new immunization-specific document database. The resulting optimized NLP model at the core of this system is able to identify and extract PICO-related fields from publications on immunization with an average accuracy of 88% across five classes of text. Functionality is provided through a straightforward web interface.

UNASSIGNED: Since its release in November 2022, Chat Generative Pre-Trained Transformer 3.5 (ChatGPT), a complex machine learning model, has garnered more than 100 million users worldwide. The aim of this study is to determine how well ChatGPT can generate novel systematic review ideas on topics within spine surgery.
UNASSIGNED: ChatGPT was instructed to give ten novel systematic review ideas for five popular topics in spine surgery literature: microdiscectomy, laminectomy, spinal fusion, kyphoplasty and disc replacement. A comprehensive literature search was conducted in PubMed, CINAHL, EMBASE and Cochrane. The number of nonsystematic review articles and number of systematic review papers that had been published on each ChatGPT-generated idea were recorded.
UNASSIGNED: Overall, ChatGPT had a 68% accuracy rate in creating novel systematic review ideas. More specifically, the accuracy rates were 80%, 80%, 40%, 70% and 70% for microdiscectomy, laminectomy, spinal fusion, kyphoplasty and disc replacement, respectively. However, there was a 32% rate of ChatGPT generating ideas for which there were 0 nonsystematic review articles published. There was a 71.4%, 50%, 22.2%, 50%, 62.5% and 51.2% success rate of generating novel systematic review ideas, for which there were also nonsystematic reviews published, for microdiscectomy, laminectomy, spinal fusion, kyphoplasty, disc replacement and overall, respectively.
UNASSIGNED: ChatGPT generated novel systematic review ideas at an overall rate of 68%. ChatGPT can help identify knowledge gaps in spine research that warrant further investigation, when used under supervision of an experienced spine specialist. This technology can be erroneous and lacks intrinsic logic; so, it should never be used in isolation.
UNASSIGNED: Not applicable.

Over the past three decades, Individual Placement and Support (IPS) has emerged as a robust evidence-based approach to helping people with severe mental illnesses, such as schizophrenia, bipolar disorder, and major depression, to obtain and succeed in competitive employment. This review addresses the history, principles, research, and future directions of IPS. It covers current evidence on employment outcomes, cost-effectiveness, and nonvocational outcomes. It also describes current attempts to extend IPS to new populations. The authors provide an overview of numerous systematic reviews and meta-analyses of randomized controlled trials involving people with serious mental illness. For studies addressing nonvocational outcomes and new populations, the review uses best available evidence. Published reviews agree that IPS enables patients with serious mental illness in high-income countries to succeed in competitive employment at a higher rate than patients who receive other vocational interventions. Within IPS programs, quality of implementation, measured by standardized fidelity scales, correlates with better outcomes. Employment itself leads to enhanced income, psychosocial outcomes, clinical improvements, and decreased mental health service use. As IPS steadily spreads to new populations and new settings, research is active across high-income countries and spreading slowly to middle-income countries. IPS is an evidence-based practice for people with serious mental illness in high-income countries. It shows promise to help other disability groups also, and emerging research aims to clarify adaptations and outcomes.