Subspecialty Influence on Scientific Peer Review for an Obstetrics and Gynecology Journal With a High Impact Factor
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether quality of peer review and reviewer recommendation differ based on reviewer subspecialty in obstetrics and gynecology and to determine the role of experience on reviewer recommendation. METHODS: We performed a retrospective cohort study of reviews submitted to Obstetrics & Gynecology between January 2010 and December 2014. Subspecialties were determined based on classification terms selected by each reviewer and included all major obstetrics and gynecology subspecialties, general obstetrics and gynecology, and nonobstetrics and gynecology categories. Review quality (graded on a 5-point Likert scale by the journal's editors) and reviewer recommendation of "reject" were compared across subspecialties using χ, analysis of variance, and multivariate logistic regression. RESULTS: There were 20,027 reviews from 1,889 individual reviewers. Reviewers with family planning subspecialty provided higher-quality peer reviews compared with reviewers with gynecology only, reproductive endocrinology and infertility, gynecologic oncology, and general obstetrics and gynecology specialties (3.61±0.75 compared with 3.44±0.78, 3.42±0.72, 3.35±0.75, and 3.32±0.81, respectively, P<.05). Reviewers with gynecology-only subspecialty recommended rejection more often compared with reviewers with a nonobstetrics and gynecology subspecialty (57.7% compared with 38.7%, P<.05). Editorial Board members recommended rejection more often than new reviewers (68.0% compared with 41.5%, P<.05). Increased adjusted odds of manuscript rejection recommendation were associated with reproductive endocrinology, female pelvic medicine and reconstructive surgery, and gynecology-only reviewer subspecialty (adjusted odds ratio [OR] 1.23 [1.07-1.41], 1.21 [1.05-1.39], and 1.11 [1.02-1.20]). Manuscript rejection recommendation rate was also increased for reviewers who had completed the highest quintile of peer reviews (greater than 195) compared with the lowest quintile (one to seven) (adjusted OR 2.85 [2.60-3.12]). CONCLUSION: Peer review quality differs based on obstetrics and gynecology subspecialty. Obstetrics and gynecology subspecialty and reviewer experience have implications for manuscript rejection recommendation. Reviewer assignment is pivotal to maintaining a rigorous manuscript selection process.
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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 | 0.135 | 0.646 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".