Hospital and Surgeon Determinants of Carotid Endarterectomy Outcomes
Bibliographic record
Abstract
BACKGROUND: Prior studies have found associations between surgeon and hospital case volumes and outcomes after carotid endarterectomy (CEA), but they have not simultaneously assessed the importance of a number of surgeon and hospital characteristics. OBJECTIVE: To simultaneously assess associations between hospital case volume, teaching status, clinical trial participation, and surgeon specialty and case volume and the outcome after CEA. DESIGN: Analysis of a large administrative data-base using logistic regression to correlate adverse outcomes after CEA with surgeon and hospital characteristics. SETTING AND PATIENTS: A Canadian administrative hospital discharge database of all patients undergoing CEA in fiscal years 1994 through 1997. MAIN OUTCOME MEASURES: In-hospital stroke and/or death. RESULTS: We found an inverse relationship between both hospital and surgeon case volumes and adverse outcomes. Teaching status had no association with outcome, but previous clinical trial participation predicted a better outcome. General surgeons fared worse than other specialists. Low-volume surgeons in low-volume hospitals had a relative risk of 3.5 for adverse outcomes compared with high-volume surgeons in high-volume hospitals. CONCLUSIONS: Several physician and hospital characteristics are determinants of outcome after CEA, but the negative effects of low hospital and surgeon case volumes, in particular, suggest that regionalization should be considered for CEA and that surgeons with low case volumes should not be performing CEA.
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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 | 0.007 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".