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UNASSIGNED: Evaluate methodological quality of type 2 diabetes RCTs conducted in Iran and cited in clinical practice guidelines and systematic reviews and meta-analyses.
UNASSIGNED: We conducted a descriptive methodological quality review, analyzing 286 Randomized Controlled Trials (RCTs) on diabetes mellitus published in Iran from July 2004 to 2021. We searched six databases systematically and evaluated eligible articles using the CONSORT 2010 checklist for abstracts. Two investigators assessed the data using a 17-item checklist derived from CONSORT. Additionally, we examined the citations of each RCT in 260 clinical practice guidelines, with a specific focus on the adequate reporting of outcomes.
UNASSIGNED: Out of 6667 articles, 286 analyzed. Poor reporting and failure to meet criteria observed. Only 3.8% cited in guidelines. Reporting rates: primary outcomes (41.9%), randomization (61.8%), trial recruitment (12.6%), blinding (50.8%). 27.9% cited in systematic reviews, 50.34% in systematic reviews and meta-analyses, 26.57% in meta-analyses. 67.8% of papers cited in systematic reviews. Adherence highest for participants, objective, randomization, intervention, outcome; lowest for recruitment, trial design, funding source, harms, and reporting primary outcomes.
UNASSIGNED: Poor methodological reporting and adherence to CONSORT checklist in evaluated RCTs, especially in methodological sections. Improvements needed for reliable and applicable results in guidelines, reviews, and meta-analyses. Inadequate outcome reporting challenges researchers, clinicians, and policymakers, impacting evidence-based decision-making. Urgent improvements in RCT registration necessary.

As clinical trials in oncology require substantial efforts, maximizing the insights gained from them by conducting subgroup analyses is often attempted. The goal of these analyses is to identify subgroups of patients who are likely to benefit, as well as the subgroups of patients who are unlikely to benefit from the studied intervention. International guidelines occasionally include or exclude novel medications and technologies for specific subpopulations based on such analyses of pivotal trials without requiring confirmatory trials. This Perspective discusses the importance of providing a complete dataset of clinical information when reporting subgroup analyses and explains why such transparency is key for better clinical interpretation of the results and the appropriate application to clinical care, by providing examples of transparent reporting of clinical studies and examples of incomplete reporting of clinical studies.

Tumor mutational burden (TMB) has been recognized as a predictive biomarker for immunotherapy response in several tumor types. Several laboratories offer TMB testing, but there is significant variation in how TMB is calculated, reported, and interpreted among laboratories. TMB standardization efforts are underway, but no published guidance for TMB validation and reporting is currently available. Recognizing the current challenges of clinical TMB testing, the Association for Molecular Pathology convened a multidisciplinary collaborative working group with representation from the American Society of Clinical Oncology, the College of American Pathologists, and the Society for the Immunotherapy of Cancer to review the laboratory practices surrounding TMB and develop recommendations for the analytical validation and reporting of TMB testing based on survey data, literature review, and expert consensus. These recommendations encompass pre-analytical, analytical, and postanalytical factors of TMB analysis, and they emphasize the relevance of comprehensive methodological descriptions to allow comparability between assays.

This document has been developed to provide a guide for basic and advanced reporting in paediatric echocardiography. Furthermore, it aims to help clinicians in the interpretation of echocardiographic measurements and functional data for estimating the severity of disease in different paediatric age groups. The following topics will be reviewed and discussed in the present document: (i) the general principle in constructing a paediatric echocardiographic report, (ii) the basic elements to be included, and (iii) the potential and limitation of currently employed tools used for disease severity quantification during paediatric reporting. A guide for the interpretation of Z-scores will be provided. Use and interpretation of parameters employed for quantification of ventricular systolic function will be discussed. Difficulties in the adoption of adult parameters for the study of diastolic function and valve defects at different ages and pressure and loading conditions will be outlined, with pitfalls for the assessment listed. A guide for careful use of prediction scores for complex congenital heart disease will be provided. Examples of basic and advanced (disease-specific) formats for reporting in paediatric echocardiography will be provided. This document should serve as a comprehensive guide to (i) structure a comprehensive paediatric echocardiographic report; (ii) identify the basic morphological details, measures, and functional parameters to be included during echocardiographic reporting; and (iii) correctly interpret measurements and functional data for estimating disease severity.

Given the unpleasant nature of social isolation and loneliness (SIL) and their negative effects on health and wellbeing, interventions are needed. However, persistent issues in the design, evaluation, and reporting of SIL interventions preclude conclusive evidence and commentary on the effectiveness of SIL interventions. Here, we propose guidelines for evaluating SIL interventions, firstly by operationalising them into two categories: (1) interventions aiming to reduce SIL as a primary outcome and (2) interventions aiming to improve non-SIL outcomes in the lives of individuals experiencing SIL. Secondly, we evaluate instruments for measuring SIL and research designs for studying intervention effectiveness. Thirdly, guidelines for reporting information about the intervention, study design, results, and discussion in SIL intervention studies are presented. These guidelines will help researchers to better and more consistently report on SIL interventions and improve comparability of SIL interventions, ultimately contributing to the improvement of interventions and to the mitigation of SIL.

OBJECTIVE: Protocols are invaluable documents for any research study, especially for prediction model studies. However, the mere existence of a protocol is insufficient if key details are omitted. We reviewed the reporting content and details of the proposed design and methods reported in published protocols for prediction model research.
METHODS: We searched MEDLINE, Embase, and the Web of Science Core Collection for protocols for studies developing or validating a diagnostic or prognostic model using any modeling approach in any clinical area. We screened protocols published between Jan 1, 2022 and June 30, 2022. We used the abstract, introduction, methods, and discussion sections of The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement to inform data extraction.
RESULTS: We identified 30 protocols, of which 28 were describing plans for model development and six for model validation. All protocols were open access, including a preprint. 15 protocols reported prospectively collecting data. 21 protocols planned to use clustered data, of which one-third planned methods to account for it. A planned sample size was reported for 93% development and 67% validation analyses. 16 protocols reported details of study registration, but all protocols reported a statement on ethics approval. Plans for data sharing were reported in 13 protocols.
CONCLUSIONS: Protocols for prediction model studies are uncommon, and few are made publicly available. Those that are available were reasonably well-reported and often described their methods following current prediction model research recommendations, likely leading to better reporting and methods in the actual study.

OBJECTIVE: To determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artificial intelligence (AI) in their author and reviewer instructions.
METHODS: The primary source of journal information and associated citation data used was the Journal Citation Reports (June 2023 release for 2022 citation data; Clarivate Analytics, UK). The first- and second-quartile journals indexed in the Science Citation Index Expanded and the Emerging Sources Citation Index were included. The author and reviewer instructions were evaluated by two independent readers, followed by an additional reader for consensus, with the assistance of automatic annotation. Encouragement and submission requirements were systematically analyzed. The reporting guidelines were grouped as AI-specific, related to modeling, and unrelated to modeling.
RESULTS: Out of 102 journals, 98 were included in this study, and all of them had author instructions. Only five journals (5%) encouraged the authors to follow AI-specific reporting guidelines. Among these, three required a filled-out checklist. Reviewer instructions were found in 16 journals (16%), among which one journal (6%) encouraged the reviewers to follow AI-specific reporting guidelines without submission requirements. The proportions of author and reviewer encouragement for AI-specific reporting guidelines were statistically significantly lower compared with those for other types of guidelines (P < 0.05 for all).
CONCLUSIONS: The findings indicate that AI-specific guidelines are not commonly encouraged and mandated (i.e., requiring a filled-out checklist) by these journals, compared with guidelines related to modeling and unrelated to modeling, leaving vast space for improvement. This meta-research study hopes to contribute to the awareness of the imaging community for AI reporting guidelines and ignite large-scale group efforts by all stakeholders, making AI research less wasteful.
CONCLUSIONS: This meta-research highlights the need for improved encouragement of AI-specific guidelines in radiology, nuclear medicine, and medical imaging journals. This can potentially foster greater awareness among the AI community and motivate various stakeholders to collaborate to promote more efficient and responsible AI research reporting practices.

The sizeable body of evidence indicating that parenting programs have a positive impact on children and families highlights the potential public health benefits of their implementation on a large scale. Despite evidence and global attention, beyond the highly controlled delivery of parenting programs via randomized trials, little is known about program effectiveness or how to explain the poorer results commonly observed when implemented in community settings. Researchers, practitioners, and policymakers must work together to identify what is needed to spur adoption and sustainment of evidence-based parenting programs in real-world service systems and how to enhance program effectiveness when delivered via these systems. Collecting, analyzing, and using facilitator fidelity data is an important frontier through which researchers and practitioners can contribute. In this commentary, we outline the value of assessing facilitator fidelity and utilizing the data generated from these assessments; describe gaps in research, knowledge, and practice; and recommend directions for research and practice. In making recommendations, we describe a collaborative process to develop a preliminary guideline-the Fidelity of Implementation in Parenting Programs Guideline or FIPP-to use when reporting on facilitator fidelity. Readers are invited to complete an online survey to provide comments and feedback on the first draft of the guideline.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s43477-023-00092-5.

BACKGROUND: Recommendations within clinical practice guidelines (CPGs) are heavily influenced by results from randomized controlled trials (RCTs). Therefore, it is imperative that all RCT outcomes are reported thoroughly to ensure CPGs are created using accurate information. Here, we evaluate the quality of harms reporting using the CONSORT Extension for Harms in RCTs underpinning recommendations in the American Academy of Orthopedic Surgeons (AAOS) Management of Hip Fractures in Older Adults CPG.
METHODS: Each RCT cited as evidence for recommendations in the AAOS Management of Hip Fractures in Older Adults CPG was evaluated using the CONSORT Extension for Harms to determine the quality of harms reporting. Descriptive statistics (frequencies, percentages, 95 % confidence intervals) were used to summarize adherence to CONSORT Harms items. A linear regression model was used to evaluate the CONSORT Harms influence on the quality of reporting over time.
RESULTS: Among the 156 RCTs identified, there were a total of 31,848 participants. Most RCTs were conducted at a single center (137; 87.8 %) and in a single-blind manner (130; 83.3 %). Fifty-four (34.6 %) RCTs did not provide funding statements. Trials adequately reported an average of 6.65 out of 18 CONSORT Extension for Harms items (37.0 %). One RCT adequately reported all items, while five reported zero items. Forty-seven RCTs (30.1 %) reported ≥ 50 % of items and 73 (46.8 %) reported ≤ 33.3 % of items. The linear regression model demonstrated no significant increase in mean adherence over time (adjusted R2 = -0.006; p = 0.563).
CONCLUSIONS: Our results highlight inadequate harms reporting among RCTs in the AAOS Management of Hip Fractures in Older Patients CPG. While the CONSORT Harms Extension was intended to enhance reporting, the linear regression model did not demonstrate significant improvements over time.