Journal of clinical epidemiology
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This study developed, calibrated, and evaluated a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews. ⋯ The Cochrane RCT Classifier can reduce manual study identification workload for Cochrane Reviews, with a very low and acceptable risk of missing eligible RCTs. This classifier now forms part of the Evidence Pipeline, an integrated workflow deployed within Cochrane to help improve the efficiency of the study identification processes that support systematic review production.
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Filtering the deluge of new research to facilitate evidence synthesis has proven to be unmanageable using current paradigms of search and retrieval. Crowdsourcing, a way of harnessing the collective effort of a "crowd" of people, has the potential to support evidence synthesis by addressing this information overload created by the exponential growth in primary research outputs. Cochrane Crowd, Cochrane's citizen science platform, offers a range of tasks aimed at identifying studies related to health care. Accompanying each task are brief, interactive training modules, and agreement algorithms that help ensure accurate collective decision-making.The aims of the study were to evaluate the performance of Cochrane Crowd in terms of its accuracy, capacity, and autonomy and to examine contributor engagement across three tasks aimed at identifying randomized trials. ⋯ Cochrane Crowd is sufficiently accurate and scalable to keep pace with the current rate of publication (and registration) of new primary studies. It has also proved to be a popular, efficient, and accurate way for a large number of people to play an important voluntary role in health evidence production. Cochrane Crowd is now an established part of Cochrane's effort to manage the deluge of primary research being produced.
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To examine whether the use of natural language processing (NLP) technology is effective in assisting rapid title and abstract screening when updating a systematic review. ⋯ NLP technology using the ensemble learning method may effectively assist in rapid literature screening when updating systematic reviews.
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This text builds on a conversation with Dr. Gordon Guyatt who provided guidance to trainees on interdisciplinary collaboration and work-life balance as part of a graduate-level biostatistics course in September 2020. The participants in the conversation comprised a group of clinicians, methodologists and biostatisticians, ranging from junior trainees to world-renowned established researchers. ⋯ As an invited guest, Dr. Guyatt shares his experiences after decades of leadership and successful international collaborations. He emphasizes the importance finding the right mentor, forming productive collaborations, developing self-awareness, and strategies for achieving a work-life balance.