Preprint server use in kidney disease research: a rapid review
Bibliographic record
Abstract
Abstract Purpose of review Preprint servers including arXiv and bioRxiv have disrupted the scientific communication landscape by providing rapid access to pre--peer reviewed research. MedRxiv is a recently launched free online repository for preprints in the health sciences. We sought to summarize potential benefits and risks to preprint server use, from both the researcher and end--user perspective, and evaluate the uptake of preprint servers in the nephrology community. Sources of Information We performed a rapid review of articles describing preprint servers and their use. We approached the 20 highest impact nephrology journals regarding their policy towards the use of preprint servers. We evaluated the average time from study completion to publication of impactful articles in nephrology. Finally, we evaluated the number of nephrology articles submitted to preprint servers. Findings To date over 600 kidney--related articles have been uploaded to bioRxiv and medRxiv. The average time from study completion to publication was over 10 months. 16 of the top 20 nephrology journals currently accept research submitted to a preprint server. Transparency and collaboration, visibility and recognition, and rapid dissemination of results were identified as benefits of preprint servers. Concerns exist regarding the potential risk of non--peer reviewed medical research being publicly available. Limitations Preprint servers remain a recent phenomenon in health sciences and their long-- term impact on the medical literature remains to be seen. Implications The quantity of research submitted to preprint servers is likely to continue to grow. The model for dissemination of research results will need to adapt to incorporate preprint servers.
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.
How this classification was reachedexpand
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 | 0.039 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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; both teacher heads agree on what is shown here.
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".