Surgeon Case Volume Impacts Revision Rate of Endoscopic Sinus Surgery
Bibliographic record
Abstract
INTRODUCTION: Previous studies have demonstrated a positive relationship between surgeon volume and patient outcomes. While this relationship has been established in oncologic, bariatric, and orthopedic surgeries, little is known in the realm of endoscopic sinus surgery (ESS). The objective was to assess the association between a surgeon's ESS annual volume and rates of revision surgery within 5 years as well as 30-day complications for patients with chronic rhinosinusitis (CRS). METHODS: We identified CRS patients in Ontario, Canada, who underwent primary ESS, using surgeon-level administrative data between 2014 and 2018 (N = 13,562). Surgeon volume was calculated based on the number of procedures performed in the previous year by the surgeon and was divided into quartiles. We identified those who underwent revision ESS within the subsequent 5-year period. Complications were defined as unplanned hospital admission or emergency department visit within the first 30 days following the operation. A multivariate regression model was used to estimate the effect of surgeon volume on revision rate. RESULTS: An unadjusted model demonstrated that high surgeon volume quartile (> 63/year) was associated with lower rates of revision surgery and 30-day hospitalization (p < 0.05 for both) along with a higher rate of complete ESS (p < 0.001). After controlling for patient/surgeon covariates and extent of ESS, low-volume surgeons (1-17/year) remained an independent statistically significant predictor of revision surgery (hazard ratios [HR]: 1.60, 95% confidence interval [CI]: 1.17-2.19). CONCLUSION: Our study is the first to demonstrate a surgeon volume-outcome relationship in ESS. Being a high-volume surgeon is predictive of a lower revision rate in CRS patients undergoing ESS.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".