563 VARIATION IN TREATMENT PATTERNS AND OUTCOMES FOR RESECTED ESOPHAGEAL CANCER AT DESIGNATED THORACIC SURGERY CENTERS
Bibliographic record
Abstract
Abstract Ontario defined designated thoracic surgery centres to provide high-volume care for patients undergoing esophageal cancer resection. The objective of this study was to compare thoracic centres’ performance to non-thoracic centres, and to assess variation in treatment patterns and outcomes across thoracic centres. Methods A retrospective cohort study (2002–2014) was conducted in Ontario, Canada (population 13.6 million), examining adults with resected esophageal cancer. Case mix, use of neoadjuvant therapy, surgical outcomes (lymph node yield and positive margin rates) and survival were described across the 15 thoracic centres. Multivariable regression was used to estimate the effect of having surgery at designated thoracic centres on postoperative (in-hospital & 90-day post-discharge) mortality and overall 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. Patients at thoracic centres (82.6%) received more neoadjuvant therapy, but there was no difference in positive margin rates, lymph node harvest, postoperative mortality and overall survival between thoracic and non-thoracic centres. Across thoracic centres, rates of neoadjuvant therapy varied from 16.4–81.6%, positive margin rates varied from 8.2–29.6%, median lymph node harvest varied from 7–20 nodes, postoperative mortality varied from 0–18.7%, and median survival varied from 17–26 months. Conclusion There was significant variability in treatment patterns, surgical outcomes, and survival among patients treated at designated thoracic centres. Feedback of patient outcomes to surgeons and hospitals, and translating best practices from high-performing hospitals to other hospitals, is the next step in improving 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 | 0.001 |
| 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) | 0.002 | 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".