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The most common primary brain tumor is glioblastoma (GBM), yet the current therapeutic options for this disease are not promising. Although immunotherapeutic techniques have shown poor success in GBM thus far despite efforts, new developments provide optimism. One of these developments is chimeric antigen receptor (CAR)-T cell treatment, which includes removing and genetically modifying autologous T cells to produce a receptor that targets a GBM antigen before reintroducing the cells into the patient\'s body. A number of preclinical studies have produced encouraging results, which have led to the start of clinical trials assessing these CAR-T cell treatments for GBM and other brain tumors. Although results in tumors such as diffuse intrinsic pontine gliomas and lymphomas have been promising, preliminary findings in GBM have not produced any clinical benefits. The paucity of particular antigens in GBM, their inconsistent expression patterns, and the possible immunoediting-induced loss of these antigens after antigen-targeted therapy are some possible causes for this discrepancy. The goal of this systematic literature review is to assess potential approaches for creating CAR-T cells that are more effective for this indication, as well as the clinical experiences that are already being had with CAR-T cell therapy in GBM. Up until 9 May 2024, a thorough search was carried out across the three main medical databases: PubMed, Web of Science, and Scopus. Relevant Medical Subject Heading (MeSH) terms and keywords associated with \"glioblastoma\", \"CAR-T\", \"T cell therapy\", \"overall survival\", and \"progression free survival\" were employed in the search approach. Preclinical and clinical research on the application of CAR-T cells as a therapeutic approach for GBM are included in the review. A total of 838 papers were identified. Of these, 379 articles were assessed for eligibility, resulting in 8 articles meeting the inclusion criteria. The included studies were conducted between 2015 and 2023, with a total of 151 patients enrolled. The studies varied in CAR-T cell types. EGFRvIII CAR-T cells were the most frequently investigated, used in three studies (37.5%). Intravenous delivery was the most common method of delivery (62.5%). Median OS ranged from 5.5 to 11.1 months across the studies. PFS was reported in only two studies, with values of 7.5 months and 1.3 months. This systematic review highlights the evolving research on CAR-T cell therapy for GBM, emphasizing its potential despite challenges. Targeting antigens like EGFRvIII and IL13Rα2 shows promise in treating recurrent GBM. However, issues such as antigen escape, tumor heterogeneity, and immunosuppression require further optimization. Innovative delivery methods, combination therapies, and personalized approaches are crucial for enhancing CAR-T cell efficacy. Ongoing research is essential to refine these therapies and improve outcomes for GBM patients.

BACKGROUND: Considering the high rates of persistent tobacco use, effective cessation interventions are needed for cancer patients and caregivers. Despite the need, there is a significant lack of research on tobacco cessation, especially for non-respiratory cancers (breast, prostate, colorectal, cervical, and bladder cancer).
OBJECTIVE: The objective was to evaluate tobacco use and tobacco cessation interventions among patients and caregivers for non-respiratory cancers.
METHODS: Randomized controlled trials assessing tobacco cessation interventions were identified. Five electronic databases were searched in accordance with the Preferred Reporting Items for Systematic reviews and Meta-analyses guidelines through July 2023. Studies exclusive to lung, oral, thoracic, and head and neck cancers were excluded. Effect sizes were estimated; risk of bias was assessed.
RESULTS: Of 3,304 studies, 17 were included. Interventions included behavioral (n = 6), pharmacotherapy (n = 2), and a combination (n = 9) treatment. Eight studies included a health behavior model; mean behavioral change techniques were 5.57. Pooled magnitude of the odds of cessation was positive and significant (odds ratio = 1.24, 95% confidence interval [Lower Limit 1.02, Upper Limit 1.51]) relative to usual care/placebo. Cumulative meta-analysis examined the accumulation of results over-time and demonstrated that studies have been significant since 2020. Two studies included caregivers\' who were involved in the provision of social support.
CONCLUSIONS: Current interventions have the potential to reduce tobacco use in non-respiratory cancers. Results may be beneficial for promoting tobacco cessation among non-respiratory cancers. There is a considerable lack of dyadic interventions for cancer survivors and caregivers; researchers are encouraged to explore dyadic approaches.
We aimed to understand effective ways for cancer patients and caregivers to quit using tobacco. We focused on non-respiratory cancers (cancers not related to breathing issues) like breast, prostate, and colorectal cancer. We reviewed 17 randomized controlled trials designed to help people quit tobacco, which included behavioral therapies (e.g., education and counseling), pharmacotherapy (i.e., medicine), and combinations of both. We found that people in these studies quit using tobacco, especially when more than one approach was used. The studies also showed that these approaches have been more successful since 2020. The research highlighted a need for more studies that include both patients and their caregivers together in the quitting process. This approach, called dyadic intervention, could be more effective in supporting patients and their caregivers. Overall, while the current approaches are promising, more research is needed to develop better ways to help cancer patients and caregivers quit smoking for longer.

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.

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.

BACKGROUND: This review aims to synthesise the literature on the efficacy, evolution, and challenges of implementing Clincian Decision Support Systems (CDSS) in the realm of mental health, addiction, and concurrent disorders.
METHODS: Following PRISMA guidelines, a systematic review and meta-analysis were performed. Searches conducted in databases such as MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science through 25 May 2023, yielded 27,344 records. After necessary exclusions, 69 records were allocated for detailed synthesis. In the examination of patient outcomes with a focus on metrics such as therapeutic efficacy, patient satisfaction, and treatment acceptance, meta-analytic techniques were employed to synthesise data from randomised controlled trials.
RESULTS: A total of 69 studies were included, revealing a shift from knowledge-based models pre-2017 to a rise in data-driven models post-2017. The majority of models were found to be in Stage 2 or 4 of maturity. The meta-analysis showed an effect size of -0.11 for addiction-related outcomes and a stronger effect size of -0.50 for patient satisfaction and acceptance of CDSS.
CONCLUSIONS: The results indicate a shift from knowledge-based to data-driven CDSS approaches, aligned with advances in machine learning and big data. Although the immediate impact on addiction outcomes is modest, higher patient satisfaction suggests promise for wider CDSS use. Identified challenges include alert fatigue and opaque AI models.
CONCLUSIONS: CDSS shows promise in mental health and addiction treatment but requires a nuanced approach for effective and ethical implementation. The results emphasise the need for continued research to ensure optimised and equitable use in healthcare settings.

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.