Sex bias in rhinology research
Bibliographic record
Abstract
Background Analysis of general surgery literature has revealed noteworthy sex bias and underreporting. Our objective was to determine the prevalence of sex bias and underreporting in rhinology. Methods All articles in 2016 issues of Rhinology, the American Journal of Rhinology and Allergy (AJRA), and the International Forum of Allergy and Rhinology (IFAR) were reviewed. Of 369 articles, 248 met inclusion criteria. Excluded studies were cadaveric, meta‐analysis/review, and editorial. Data collected included study type, demographics, and sex‐based statistical analysis. Results There were 202 clinical and 46 basic science/translational studies. From 188 of 202 clinical studies with known sex, 1 included participants of a single sex. Sex matching >50% (SM50) was found in 81.9%, and 55.9% performed sex‐based statistical analysis. Domestic clinical studies performed sex‐based analysis more frequently than international (54.9% vs 44.4%) and exhibited a higher rate of SM50 (84.5% vs 80.3%), though these differences were not statistically significant. For basic/translational studies, 54.5% (24/44) provided sex breakdown. Among these, 29.2% included 1 sex, and 8.3% performed sex‐based analysis. Of 10 using animals, 70.0% utilized 1 sex. The remaining 30.0% did not report sex. None of 4 cell line studies reported cell sex. Less than half (46.2%) of domestic and 56.3% of international studies reported sex breakdown; 7.7% of domestic and 3.0% of international studies performed sex‐based analysis. Conclusion Although sex may impact outcomes, research without sex reporting and analysis is prevalent, particularly among basic science/translational studies. Future research must account for sex in demographics and analysis to best inform evidence‐based clinical guidelines.
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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 | 0.217 | 0.396 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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; the direct Gemma label and the distilled Codex classifier 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".