Preprint Servers in Kidney Disease Research
Bibliographic record
Abstract
Preprint servers, such as arXiv and bioRxiv, have disrupted the scientific communication landscape by providing rapid access to research before peer review. medRxiv was launched as a free online repository for preprints in the medical, clinical, and related health sciences in 2019. In this review, we present the uptake of preprint server use in nephrology and discuss specific considerations regarding preprint server use in medicine. Distribution of kidney-related research on preprint servers is rising at an exponential rate. Survey of nephrology journals identified that 15 of 17 (88%) are publishing original research accepted submissions that have been uploaded to preprint servers. After reviewing 52 clinically impactful trials in nephrology discussed in the online Nephrology Journal Club (NephJC), an average lag of 300 days was found between study completion and publication, indicating an opportunity for faster research dissemination. Rapid review of papers discussing benefits and risks of preprint server use from the researcher, publisher, or end user perspective identified 53 papers that met criteria. Potential benefits of biomedical preprint servers included rapid dissemination, improved transparency of the peer review process, greater visibility and recognition, and collaboration. However, these benefits come at the risk of rapid spread of results not yet subjected to the rigors of peer review. Preprint servers shift the burden of critical appraisal to the reader. Media may be especially at risk due to their focus on "late-breaking" information. Preprint servers have played an even larger role when late-breaking research results are of special interest, such as during the global coronavirus disease 2019 pandemic. Coronavirus disease 2019 has brought both the benefits and risks of preprint servers to the forefront. Given the prominent online presence of the nephrology community, it is poised to lead the medicine community in appropriate use of preprint servers.
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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 | 0.050 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.033 | 0.016 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.280 | 0.277 |
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".