Surgeon Thyroidectomy Case Volume Impacts Disease‐free Survival in the Management of Thyroid Cancer
Bibliographic record
Abstract
OBJECTIVES: To assess the association between surgeons thyroidectomy case volume and disease-free survival (DFS) for patients with well-differentiated thyroid cancer (WDTC). A secondary objective was to assess a surgeon volume cutoff to optimize outcomes in those with WDTC. We hypothesized that surgeon volume will be an important predictor of DFS in patients with WDTC after adjusting for hospital volume and sociodemographic and clinical factors. METHODS: In this retrospective population-based cohort study, we identified WDTC patients in Ontario, Canada, who underwent thyroidectomy confirmed by both hospital-level and surgeon-level administrative data between 1993 and 2017 (N = 37,233). Surgeon and hospital volumes were calculated based on number of cases performed in the year prior by the physician and at an institution performing each case, respectively and divided into quartiles. A multilevel hierarchical Cox regression model was used to estimate the effect of volume on DFS. RESULTS: A crude model without patient or treatment characteristics demonstrated that both higher surgeon volume quartiles (p < 0.001) and higher hospital volume quartiles (p < 0.001) were associated with DFS. After controlling for clustering and patient/treatment covariates and hospital volume, moderately low (18-39/year) and low (0-17/year) volume surgeons (hazard ratios [HR]: 1.23, 95% confidence interval [CI]: 1.09-1.39 and HR: 1.34, 95% CI: 1.17-1.53 respectively) remained an independent statistically significant negative predictor of DFS. CONCLUSION: Both high-volume surgeons and hospitals are predictors of better DFS in patients with WDTC. DFS is higher among surgeons performing more than 40 thyroidectomies a year. LEVEL OF EVIDENCE: 3 Laryngoscope, 133:S1-S15, 2023.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".