The Association Between Online Surgeon Ratings and Patients’ Postoperative Outcomes in the United States
Bibliographic record
Abstract
Objective: To determine whether ratings are associated with postoperative outcomes. Background: Online ratings by patients or inclusion on lists of exceptional physicians are publicly available. Methods: In this retrospective study, Medicare fee-for-service beneficiaries 65 to 99 years old undergoing one of 14 major (elective/emergent) surgeries in the United States between 2016 and 2019 were analyzed. Data were analyzed from September 2023 to March 2024. Using computational methods to extract surgeon ratings from the three highest-volume publicly available patient-initiated and peer-nominated rating platforms. The exposure of interest was ratings (0-4, 4-4.49, ≥ 4.5) on patient-initiated platforms and "Top Doctor" status on the peer-nominated platform. The primary outcome was 30-day mortality. Secondary outcomes included 30-day complications, readmission, failure to rescue, and hospital length of stay. Using linear probability models, we controlled for patient, surgeon, and hospital factors to examine associations between ratings and outcomes. Results: We identified 2,690,315 patients operated on by 57,008 surgeons. Patient-initiated ratings were not consistently associated with 30-day mortality but were significantly associated with lower mortality among those treated by surgeons rated 4 to 4.49 on Platform B [adjusted risk difference (ARD), -0.06 % (95% confidence interval (CI) = -0.11 to -0.01)]. Patients treated by "Top Doctor" surgeons through peer-nomination had lower 30-day mortality ARD, -0.14 % (95% CI = -0.19 to -0.09). Surgeons with higher patient-initiated ratings had lower rates of 30-day complications and readmissions, while "Top Doctors" experienced lower rates of failure to rescue. Conclusions: Patient-initiated and peer-nominated ratings were associated with complications and readmission; mortality and failure to rescue, respectively, suggesting they capture different aspects of surgical care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".