Preprint servers in lipidology: current status and future role
Bibliographic record
Abstract
Purpose of review Preprinting, or the sharing of non-peer reviewed, unpublished scholarly manuscripts, has exploded in all fields of science and medicine over the past 5 years. We searched the literature and evaluated the posting and uptake of preprint publications in the field of lipidology in bioRxiv and medRxiv servers. We also contacted the editorial offices of 20 journals that publish original research in lipidology to gauge their policies on preprints. Recent findings All 20 journals contacted indicated that they accepted preprints. As of 31 May 2021, 473 and 231 preprints in lipidology had been submitted to bioRxiv and medRxiv, respectively. About half of all lipidology preprints were related to cardiovascular, cardiometabolic, and/or metabolic diseases (CVMD) and their risk factors, but at least 12 other disease categories were also represented. 16.9% and 1.08% of medRxiv and bioRxiv preprints, respectively, were related to coronavirus disease 2019 (COVID-19). Summary All identified journals accept lipidology themed preprints for submission, removing any barriers authors may have had regarding preprinting. Based on growing experience with preprinting, this trend should encourage increased community feedback and facilitate higher quality lipidology research in the future.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".