Variation in treatment patterns and outcomes for resected esophageal cancer at designated thoracic surgery centers.
Bibliographic record
Abstract
343 Background: Ontario regionalized thoracic surgery to designated centers to provide high-volume care for patients undergoing esophageal cancer resection. The objective of this study was to assess variation in treatment patterns and outcomes across thoracic centers, and to compare their performance to non-thoracic centers. Methods: A retrospective, population-based cohort study (2002-2014) was conducted in Ontario, Canada (population 13.6 million). Adults with resected esophageal cancer were identified through the PRESTO database. Case mix, use of neoadjuvant therapy, surgical outcomes (lymph node yield and margin rates) and survival were described across thoracic centers. Multivariable regression was used to estimate the effect of having surgery at a regionalized thoracic surgery center on perioperative (in-hospital & 90-day post-discharge) mortality and long-term survival, adjusting for case mix. Results: Of 3,880 patients meeting study criteria, 2,213 had pathology data available and were included in the analysis. Average age was 64 years, 85.7% had adenocarcinoma, 50.2% were pT3, and 38.4% were pN0. Most (82.6%) had surgery at one of 15 thoracic centers. Across thoracic centers, rates of neoadjuvant therapy varied 16.4-81.6%, positive margin rates varied 8.2-29.6%, median lymph node harvest varied from 7-20 nodes, perioperative mortality varied 2.6-20.5%, and 2-year survival varied from 48-80%. There was a trend toward reduced perioperative mortality, but no difference in long-term survival, with having surgery at a thoracic center. Conclusions: Even at designated thoracic centers, there is significant variability in treatment patterns, surgical outcomes, and survival. Looking beyond center volume, and translating best practices from high-performing hospitals to other hospitals, may improve patient outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".