Describing the landscape of medical education preprints on medRxiv
Bibliographic record
Abstract
Abstract Introduction A preprint is a version of a research manuscript posted to a preprint server prior to peer review. Preprints enable authors to quickly and openly share research, afford opportunities for expedient feedback, and enable immediate listing of research on grant and promotion applications. In medical education, most journals welcome preprints, suggesting they play a role in the field’s discourse. Yet, little is known about medical education preprints, including author characteristics, use, and ultimate publication status. This study provides an overview of preprints in medical education in an effort to better understand their role in the field’s discourse. Methods The authors queried medRxiv, a preprint repository, to identify preprints categorized as Medical Education and downloaded the related metadata. CrossRef was queried to gather information on preprints later published in journals. Results Between 2019-2022, 204 preprints were classified in medRxiv as Medical Education with most deposited in 2021 (n=76, 37.3%). On average, preprint full-texts were downloaded 1875.2 times, and all were promoted on social media. Preprints were authored, on average, by 5.9 authors. Corresponding authors were based in 41 countries with nearly half (45.6%) in the United States, United Kingdom, and Canada. Almost half (n=101, 49.5%) of preprints became published articles in predominantly peer-reviewed journals. Preprints appeared in 65 peer-reviewed journals with BMC Medical Education (n=9, 8.9%) most represented. Discussion Medical education research is being deposited as preprints, which are promoted, heavily accessed, and subsequently published in peer-reviewed journals, including those specific to medical education. Considering the benefits of preprints and slowness of medical education publishing, it is likely that preprint deposition will increase and preprints will be integrated into the field’s discourse. Based on these findings, we propose next steps to facilitate the responsible and effective creation and use of preprints in medical education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.062 | 0.177 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".