Geographic Variation in Surgical Practice Patterns and Outcomes for Resected Nonmetastatic Gastric Cancer in Ontario
Bibliographic record
Abstract
Background: Gastrectomy with negative resection margins and adequate lymph node dissection is the cornerstone of curative treatment for gastric cancer (GC). However, gastrectomy is a complex and invasive operation with significant morbidity and mortality. Little is known about surgical practice patterns or short- and long-term outcomes in earlystage GC in Canada. Methods: We undertook a population-based retrospective cohort study of patients with GC diagnosed between 1 April 2005 and 31 March 2008. Chart review provided clinical and operative details such as disease stage, primary tumour location, surgical approach, operation, lymph nodes, and resection margins. Administrative data provided patient demographics, geography, and vital status. Variations in treatment and outcomes were compared for 14 local health integration networks. Descriptive statistics and log-rank tests were used to examine geographic variation. Results: We identified 722 patients with nonmetastatic resected GC. We documented significant provincial variation in case mix, including primary tumour location, stage at diagnosis, and tumour grade. Short-term surgical outcomes varied across the province. The percentage of patients with 15 or fewer lymph nodes removed and examined varied from 41.8% to 73.8% (p = 0.02), and the rate of positive surgical margins ranged from 15.2% to 50.0% (p = 0.002). The 30-day surgical mortality rates did not vary statistically significantly across the province (p = 0.13); however, rates ranged from 0% to 16.7%. Overall 5-year survival was 44% and ranged from 31% to 55% across the province. Conclusions: This cohort of patients with resected stages I–III GC is the largest analyzed in Canada, providing important historical information about treatment outcomes. Understanding the causes of regional variation will support interventions aiming to improve GC operative outcomes in the cancer system.
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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 | 0.003 |
| 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".