Citation rules through the eyes of biomedical journal editors
Bibliographic record
Abstract
Abstract This research analysed the citations styles used in 1,100 high‐impact biomedical journals and the importance attributed by their editors and other members of the editorial office to individual reference components, the citation format and method. We found 70 (6.5%) use the current American Medical Association or NLM/Vancouver style; 425 (39.2%) use their older versions or a variation; 73 (6.7%) use a standard non‐biomedical style; and 432 (39.9%) have their own house style. According to 125 respondents who answered the survey, the most important reference components include the author(s), title and year of publication, while the date of update, date of access and language are among the least important. They prefer the citation‐sequence method (65.6%) and the author‐date method (24%). A comparison of the responses to the survey and the citation guidelines showed that while two‐thirds of the respondents view the DOI and ISBN as important information, only a limited number of their journals' citation guidelines require them. Our results show that publishers, authors of standard styles and editors all agree that references should be uncomplicated and concise. A reduction in the number of various styles used might be attainable but would require an agreement between the publishers and authors of the standard styles, which would incorporate the preferences of journal editors.
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 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 | 0.067 | 0.367 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".