Describing the Landscape of Medical Education Preprints on MedRxiv: Current Trends and Future Recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 it