Describing the Landscape of Medical Education Preprints on MedRxiv: Current Trends and Future Recommendations
Bibliographic record
Abstract
PURPOSE: 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, which suggests that preprints play a role in the field's discourse. Yet, little is known about medical education preprints, including author characteristics, preprint use, and ultimate publication status. This study provides an overview of preprints in medical education to better understand their role in the field's discourse. METHOD: The authors queried medRxiv, a preprint repository, to identify preprints categorized as "medical education" and downloaded related metadata. CrossRef was queried to gather information on preprints later published in journals. Data were analyzed using descriptive statistics. RESULTS: Between 2019 and 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 1,875.2 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 45.6% in the United States, the United Kingdom, and Canada. Almost half (n = 101; 49.5%) 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. CONCLUSIONS: Medical education research is being deposited as preprints, which are promoted, heavily accessed, and subsequently published in peer-reviewed journals, including medical education journals. Considering the benefits of preprints and the slowness of medical education publishing, it is likely that preprint depositing will increase and preprints will be integrated into the field's discourse. The authors propose next steps to facilitate responsible and effective creation and use of preprints.
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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 | 0.090 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.032 | 0.045 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".