Surgeon Specialty and Operative Mortality With Lung Resection
Bibliographic record
Abstract
OBJECTIVE: We sought to examine the effect of subspecialty training on operative mortality following lung resection. SUMMARY BACKGROUND DATA: While several different surgical subspecialists perform lung resection for cancer, many believe that this procedure is best performed by board-certified thoracic surgeons. METHODS: Using the national Medicare database 1998 to 1999, we identified patients undergoing lung resection (lobectomy or pneumonectomy) for lung cancer. Operating surgeons were identified by unique physician identifier codes contained in the discharge abstract. We used the American Board of Thoracic Surgery database, as well as physician practice patterns, to designate surgeons as general surgeons, cardiothoracic surgeons, or noncardiac thoracic surgeons. Using logistic regression models, we compared operative mortality across surgeon subspecialties, adjusting for patient, surgeon, and hospital characteristics. RESULTS: Overall, 25,545 Medicare patients underwent lung resection, 36% by general surgeons, 39% by cardiothoracic surgeons, and 25% by noncardiac thoracic surgeons. Patient characteristics did not differ substantially by surgeon specialty. Adjusted operative mortality rates were lowest for cardiothoracic and noncardiac thoracic surgeons (7.6% general surgeons, 5.6% cardiothoracic surgeons, 5.8% noncardiac thoracic surgeons, P = 0.001). In analyses restricted to high-volume surgeons (>20 lung resections/y), mortality rates were lowest for noncardiac thoracic surgeons (5.1% noncardiac thoracic, 5.2% cardiothoracic, and 6.1% general surgeons) (P < 0.01 for difference between general surgeons and thoracic surgeons). In analyses restricted to high-volume hospitals (>45 lung resections/y), mortality rates were again lowest for noncardiac thoracic surgeons (5.0% noncardiac thoracic, 5.3% cardiothoracic, and 6.1% general surgeons) (P < 0.01 for differences between all 3 groups). CONCLUSIONS: Operative mortality with lung resection varies by surgeon specialty. Some, but not all, of this variation in operative mortality is attributable to hospital and surgeon volume.
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".