UNASSIGNED: Recent research has shown beneficial results for music-based interventions (MBIs) for persons living with Alzheimer\'s disease and related dementias (AD/ADRD), but reports often lack sufficient detail about the MBI methodology, which reduces replicability. A detailed checklist for best practices in how to report MBIs was created in 2011 by Robb and colleagues to remedy the lack of detail in MBI descriptions. The implementation of the checklist specifically in AD/ADRD research has not been established. Given the complexity of music and the variety of uses for research and health, specific MBI descriptions are necessary for rigorous replication and validation of study results.
UNASSIGNED: This systematic mapping review utilized the \"Checklist for Reporting Music-Based Interventions\" to evaluate the current state of MBI descriptive specificity in AD/ADRD research.
UNASSIGNED: Research articles testing MBIs and reviews of MBI efficacy published between January 2015 and August 2023 were scored using the checklist and the results were summarized.
UNASSIGNED: Forty-eight studies were screened, and reporting was inconsistent across the 11 checklist criteria. Ten out of 48 studies fully reported more than 5 of the 11 criteria. Only one of the 11 scoring criteria was at least partially reported across 47 of 48 studies.
UNASSIGNED: Thorough reporting of intervention detail for MBIs remains limited in AD/ADRD MBI research. This impedes study validation, replication, and slows the progress of research and potential application of music in practice. Greater implementation of the reporting guidelines provided by Robb and colleagues would move the field of MBI research for AD/ADRD forward more quickly and efficiently.

OBJECTIVE: In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (EHR). Artificial intelligence (AI) offers a promising means of centralizing data collection and analysis, but to date, most AI algorithms have only been applied to analyzing image data in ophthalmologic practice. In this review we aimed to characterize the use of AI in the analysis of EHR, and to critically appraise the adherence of each included study to the CONSORT-AI reporting guideline.
METHODS: A comprehensive search of three relevant databases (MEDLINE, EMBASE, and Cochrane Library) from January 2010 to February 2023 was conducted. The included studies were evaluated for reporting quality based on the AI-specific items from the CONSORT-AI reporting guideline.
RESULTS: Of the 4,968 articles identified by our search, 89 studies met all inclusion criteria and were included in this review. Most of the studies utilized AI for ocular disease prediction (n = 41, 46.1%), and diabetic retinopathy was the most studied ocular pathology (n = 19, 21.3%). The overall mean CONSORT-AI score across the 14 measured items was 12.1 (range 8-14, median 12). Categories with the lowest adherence rates were: describing handling of poor quality data (48.3%), specifying participant inclusion and exclusion criteria (56.2%), and detailing access to the AI intervention or its code, including any restrictions (62.9%).
CONCLUSIONS: In conclusion, we have identified that AI is prominently being used for disease prediction in ophthalmology clinics, however these algorithms are limited by their lack of generalizability and cross-center reproducibility. A standardized framework for AI reporting should be developed, to improve AI applications in the management of ocular disease and ophthalmology decision making.

UNASSIGNED: Digital mental health interventions (DMHIs) are an effective and accessible means of addressing the unprecedented levels of mental illness worldwide. Currently, however, patient engagement with DMHIs in real-world settings is often insufficient to see clinical benefit. In order to realize the potential of DMHIs, there is a need to better understand what drives patient engagement.
UNASSIGNED: We discuss takeaways from the existing literature related to patient engagement with DMHIs and highlight gaps to be addressed through further research. Findings suggest that engagement is influenced by patient-, intervention- and systems-level factors. At the patient-level, variables such as sex, education, personality traits, race, ethnicity, age and symptom severity appear to be associated with engagement. At the intervention-level, integrating human support, gamification, financial incentives and persuasive technology features may improve engagement. Finally, although systems-level factors have not been widely explored, the existing evidence suggests that achieving engagement will require addressing organizational and social barriers and drawing on the field of implementation science.
UNASSIGNED: Future research clarifying the patient-, intervention- and systems-level factors that drive engagement will be essential. Additionally, to facilitate improved understanding of DMHI engagement, we propose the following: (a) widespread adoption of a minimum necessary 5-element engagement reporting framework; (b) broader application of alternative clinical trial designs; and (c) directed efforts to build upon an initial parsimonious conceptual model of DMHI engagement.

No formal review of qualitative research in hand surgery has been previously performed. The primary objective of this study was to evaluate the reporting quality of hand surgery qualitative research with the Standards for Reporting Qualitative Research (SRQR), a 21-item checklist. The secondary objectives were to describe qualitative research in hand surgery by domain, determine differences in reporting quality based on use of a reporting guideline, publication of SRQR and journal of publication, and to identify important outcomes in hand surgery conditions. Fifty-five studies were included from MEDLINE, Embase, PsycINFO, and Emcare. The median SRQR score was 16. The lowest reported sections were context, data collection methods, and data analysis. Qualitative research was found in multiple domains of hand surgery. There was a significant difference between papers that used a reporting guideline and studies published after the publication of the SRQR. Clinical/medical/basic science journals had the highest median SRQR score. Outcomes identified were pain for carpal tunnel syndrome and pain, function, unintentional harm, recurrence, and recovery time for Dupuytren disease. To further improve reporting quality in hand surgery qualitative research, we recommend that investigators ensure they provide rationale for their methodology and become familiar with the SRQR guidelines.

OBJECTIVE: To determine the reproducibility of biomedical systematic review search strategies.
METHODS: A cross-sectional reproducibility study was conducted on a random sample of 100 systematic reviews indexed in MEDLINE in November 2021. The primary outcome measure is the percentage of systematic reviews for which all database searches can be reproduced, operationalized as fulfilling six key Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension (PRISMA-S) reporting guideline items and having all database searches reproduced within 10% of the number of original results. Key reporting guideline items included database name, multi-database searching, full search strategies, limits and restrictions, date(s) of searches, and total records.
RESULTS: The 100 systematic review articles contained 453 database searches. Only 22 (4.9%) database searches reported all six PRISMA-S items. Forty-seven (10.4%) database searches could be reproduced within 10% of the number of results from the original search; six searches differed by more than 1,000% between the originally reported number of results and the reproduction. Only one systematic review article provided the necessary search details to be fully reproducible.
CONCLUSIONS: Systematic review search reporting is poor. To correct this will require a multifaceted response from authors, peer reviewers, journal editors, and database providers.

Concept mapping is a phased, mixed-method approach that is increasingly used in health research to develop an understanding of complex phenomena. The six phases of concept mapping are preparation, idea generation, structuring (clustering and prioritization), data analysis, interpretation, and utilization of the map. The reporting of concept mapping research requires the development of a specific reporting guideline. We conducted a systematic review to identify candidate reporting items for inclusion in a reporting guideline. Three databases (MEDLINE, CINAHL, and PsycInfo) were searched to identify studies that used concept mapping methodology. We included 75 concept mapping studies published since 2019 from which we extracted information about the quality of reporting. A third of the studies focused on public health. We identified 71 candidate items that relate to the quality of reporting concept mapping research. The rationale for the study, the focus prompt, procedures for brainstorming, and structuring statements were consistently reported across the included studies. The process for developing the focus prompt, the rationale for the size of the stakeholder groups, and the process for determining the final concept map were generally not reported. The findings from the review will be used to inform the development of our reporting guideline for concept mapping research.

Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy.

Introduction: Economic models and computer simulation models have been used for assessing short-term cost-effectiveness of interventions and modelling long-term outcomes and costs. Several guidelines and checklists have been published to improve the methods and reporting. This article presents an overview of published diabetes models with a focus on how well the models are described in relation to the considerations described by the American Diabetes Association (ADA) guidelines. Methods: Relevant electronic databases and National Institute for Health and Care Excellence (NICE) guidelines were searched in December 2012. Studies were included in the review if they estimated lifetime outcomes for patients with type 1 or type 2 diabetes. Only unique models, and only the original papers were included in the review. If additional information was reported in subsequent or paired articles, then additional citations were included. References and forward citations of relevant articles, including the previous systematic reviews were searched using a similar method to pearl growing. Four principal areas were included in the ADA guidance reporting for models: transparency, validation, uncertainty, and diabetes specific criteria. Results: A total of 19 models were included. Twelve models investigated type 2 diabetes, two developed type 1 models, two created separate models for type 1 and type 2, and three developed joint type 1 and type 2 models. Most models were developed in the United States, United Kingdom, Europe or Canada. Later models use data or methods from earlier models for development or validation. There are four main types of models: Markov-based cohort, Markov-based microsimulations, discrete-time microsimulations, and continuous time differential equations. All models were long-term diabetes models incorporating a wide range of compilations from various organ systems. In early diabetes modelling, before the ADA guidelines were published, most models did not include descriptions of all the diabetes specific components of the ADA guidelines but this improved significantly by 2004. Conclusion: A clear, descriptive short summary of the model was often lacking. Descriptions of model validation and uncertainty were the most poorly reported of the four main areas, but there exist conferences focussing specifically on the issue of validation. Interdependence between the complications was the least well incorporated or reported of the diabetes-specific criterion.

The effectiveness of transformational leadership (TFL) on various outcomes is well known. Accordingly, researchers have developed training programs to enhance TFL behaviours of leaders. Yet, no reviews summarizing the characteristics of TFL training programs exist. The purpose of this review was to examine the characteristics, reporting, and application of TFL-informed programs. A search of six databases yielded 4032 articles, 31 of which met the inclusion criteria. Program characteristics were analyzed using the Template for Intervention Description and Replication (TIDieR) checklist tool, while outcomes were analyzed according to the Kirkpatrick model of evaluation. The most common context for TFL program implementation was healthcare (n = 9). Programs were tailored and often delivered using group workshops and individual feedback. Studies reported variation in the dose of programs, rarely evaluated outcomes multiple times post-baseline, and typically employed Level 3a (subjective ratings of behaviour) evaluation measures. Reporting on program location, modifications, and fidelity was poor. Varying conceptualizations of TFL in different contexts lead to disparities in programs and protocols. Evaluation specialists should consider using tools like the TIDiER checklist to ensure that program characteristics are reported appropriately. Program planners should develop common approaches for planning and evaluating TFL programs to improve transparency and replicability of programs.

In stepped-wedge cluster randomized trials (SW-CRTs), clusters are randomized not to treatment and control arms but to sequences dictating the times of crossing from control to intervention conditions. Randomization is an essential feature of this design but application of standard methods to promote and report on balance at baseline is not straightforward. We aimed to describe current methods of randomization and reporting of balance at baseline in SW-CRTs.
We used electronic searches to identify primary reports of SW-CRTs published between 2016 and 2022.
Across 160 identified trials, the median number of clusters randomized was 11 (Q1-Q3: 8-18). Sixty-three (39%) used restricted randomization-most often stratification based on a single cluster-level covariate; 12 (19%) of these adjusted for the covariate(s) in the primary analysis. Overall, 50 (31%) and 134 (84%) reported on balance at baseline on cluster- and individual-level characteristics, respectively. Balance on individual-level characteristics was most often reported by condition in cross-sectional designs and by sequence in cohort designs. Authors reported baseline imbalances in 72 (45%) trials.
SW-CRTs often randomize a small number of clusters using unrestricted allocation. Investigators need guidance on appropriate methods of randomization and assessment and reporting of balance at baseline.