OBJECTIVE: This study provides a comprehensive overview of the knowledge structure and research hotspots regarding barriers and strategies for the implementation of clinical practice guidelines. METHODS: Publications on barriers and strategies for guideline implementation were searched for on Web of Science Core Collection from database inception to October 24, 2022. R package bibliometrix, VOSviewer, and CiteSpace were used to conduct the analysis. RESULTS: The search yielded 21,768 records from 3,975 journals by 99,998 authors from 3,964 institutions in 186 countries between 1983 and 2022. The number of published papers had a roughly increasing trend annually. The United States, the United Kingdom, and Canada contributed the majority of records. The University of Toronto, the University of Washington, and the University of Sydney were the biggest node in their cluster on the collaboration network map. The three journals that published the greatest number of relevant studies were Implementation Science, BMJ Open, and BMC Health Services Research. Grimshaw JM was the author with the most published articles, and was the second most co-cited author. Research hotspots in this field focused on public health and education, evidence-based medicine and quality promotion, diagnosis and treatment, and knowledge translation and barriers. Challenges and barriers, as well as societal impacts and inequalities, are likely to be key directions for future research. CONCLUSIONS: This is the first bibliometric study to comprehensively summarize the research trends of research on barriers and strategies for clinical practice guideline implementation. A better understanding of collaboration patterns and research hotspots may be useful for researchers. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A247.

IntroductionThere was limited evidence on the quality of reporting and methodological quality of prediction models using machine learning methods in preterm birth. This systematic review aimed to assess the reporting quality and risk of bias of a machine learning-based prediction model in preterm birth. Material and methodsWe conducted a systematic review, searching the PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disk, VIP Database, and WanFang Data from inception to September 27, 2021. Studies that developed (validated) a prediction model using machine learning methods in preterm birth were included. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and Prediction model Risk of Bias Assessment Tool (PROBAST) to evaluate the reporting quality and the risk of bias of included studies, respectively. Findings were summarized using descriptive statistics and visual plots. The protocol was registered in PROSPERO (no. CRD 42022301623). ResultsTwenty-nine studies met the inclusion criteria, with 24 development-only studies and 5 development-with-validation studies. Overall, TRIPOD adherence per study ranged from 17% to 79%, with a median adherence of 49%. The reporting of title, abstract, blinding of predictors, sample size justification, explanation of model, and model performance were mostly poor, with TRIPOD adherence ranging from 4% to 17%. For all included studies, 79% had a high overall risk of bias, and 21% had an unclear overall risk of bias. The analysis domain was most commonly rated as high risk of bias in included studies, mainly as a result of small effective sample size, selection of predictors based on univariable analysis, and lack of calibration evaluation. ConclusionsReporting and methodological quality of machine learning-based prediction models in preterm birth were poor. It is urgent to improve the design, conduct, and reporting of such studies to boost the application of machine learning-based prediction models in preterm birth in clinical practice.