Race and Ethnicity in Otolaryngology Academic Publications
Bibliographic record
Abstract
OBJECTIVE: Within otolaryngology, race is commonly included as a study covariate; however, its value in clinical practice is unclear. This study sought to explore how race and ethnicity have been used and applied over time in otolaryngology publications. DATA SOURCES: PubMed database. REVIEW METHODS: A systematic review was done to identify original otolaryngology studies between January 1, 1946, and June 25, 2020, with the following search terms: "otolaryngology" AND "race" OR "ethnicity." RESULTS: Of the 1984 yielded studies, 932 were included in the final analysis. Only 2 studies (0.2%) defined race, and 172 (18.5%) gave participants the opportunity to self-identify race. Less than half (n = 322, 43.8%) of studies controlled for confounders. One hundred studies (10.7%) linked race to genetic factors. An overall 564 (60.5%) made conclusions about race, and 232 (24.9%) mentioned that race is relevant for clinical decision making. The majority of studies had first and senior authors from high-income countries (93.9% and 93.8%, respectively). Over time, there was a significant increase in publications that controlled for confounders, the number of race categories used, and studies that highlighted disparities. CONCLUSION: Race and ethnicity are often poorly defined in otolaryngology publications. Furthermore, publications do not always control for confounding variables or allow participants to self-identify race. On the basis of our findings, we suggest 7 foundational principles that can be used to promote equitable research in otolaryngology publications. Future efforts should focus on incorporating research guidelines for race and ethnicity into journal publication standards.
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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 | 0.034 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.025 | 0.033 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".