Influence of Age, Sex, and Wait Times on Public Online Ratings of Glaucoma Physicians in the United States
Bibliographic record
Abstract
PRCIS: Glaucoma surgeons are highly rated by the general public. Physicians with shorter wait times and who are younger are more likely to have higher ratings. Female glaucoma physicians are less likely to have higher ratings. PURPOSE: Find what characteristics of glaucoma physicians are associated with higher online ratings. METHODS: All American members of the American Glaucoma Society were queried on Healthgrades, Vitals, and Yelp. Ratings, medical school ranking, region of practice, sex, age, and wait times were recorded. RESULTS: One thousand one hundred six (78.2%) of American Glaucoma Society members had at least 1 review across the 3 platforms. The average score among glaucoma surgeons was 4.160 (0.898 SD). Female physicians were associated with lower online ratings [adjusted odds ratio (aOR): 0.536; 95% CI 0.354-0.808]. Physicians with <30 minutes of wait time had higher ratings: 15-30 minutes wait time (aOR: 2.273; 95% CI: 1.430-3.636) and <15 minutes wait time (aOR: 3.102; 95% CI: 1.888-5.146). Older physicians had lower ratings (aOR: 0.384; 95% CI: 0.255-0.572). CONCLUSIONS: Public online ratings of glaucoma specialists in the United States seem to favor those of younger age, men, and those with shorter wait times.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".