Bibliographic record
Abstract
Background Esophageal cancer (EC) incidence is increasing in Ontario, and there is a need to assess outcomes for this disease on a population level. This thesis sought to (1) create a cohort of patients with EC in Ontario; (2) evaluate whether surgery at regionalized thoracic centres is associated with reduced readmission following esophageal cancer resection; and (3) compare thoracic centres’ treatment patterns, surgical outcomes, and survival to non-thoracic centres for resected EC, and assess variation across thoracic centres. Methods Using linked administrative healthcare and abstracted surgical pathology data, we created the Population Registry of Esophageal and Stomach Tumours in Ontario (PRESTO) from 2002-2014, and focused on resected EC patients. Descriptive statistics and funnel plots were used to compare treatment patterns, surgical outcomes, and survival at thoracic and non-thoracic centres, and assess variation. Multivariable logistic and Cox proportional hazards models were used to evaluate whether surgery at thoracic centres was associated with readmission, postoperative mortality, and survival. Results We identified 3,880 patients who underwent surgery for EC. Of these, 3,670 survived to discharge and were included in the readmission analysis. Overall, 27.7% were readmitted within 90 days of discharge. Surgery at thoracic centres was not significantly associated with 90-day readmission. Of 3,880 resected patients, 2,213 had pathology data available and were included in the treatment patterns, surgical outcomes, and survival analysis. Compared to patients at non-thoracic centres, patients at thoracic centres received more neoadjuvant therapy, had similar margin rates and lymph node harvest, experienced lower postoperative mortality, and had similar overall survival. Across thoracic centres, neoadjuvant therapy use 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 We created the PRESTO database to enable EC research in Ontario. Using these data, we observed patients experienced similar outcomes at thoracic and non-thoracic centres. We also documented significant variability in treatment patterns and outcomes among thoracic centres. Further quality improvement efforts are needed for surgical EC care in Ontario. The PRESTO platform can be leveraged to initiate this quality improvement work.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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".