Postoperative outcomes among Northern versus Southern Ontario patients undergoing common intermediate- to high-risk elective surgeries: a population-based cohort study
Bibliographic record
Abstract
PURPOSE: Northern Ontario residents experience multiple health disparities compared with those in Southern Ontario. It is unknown whether this leads to differences in surgical outcomes. We sought to compare postoperative outcomes of patients from Northern and Southern Ontario. METHODS: We conducted a retrospective population-based cohort study using linked administrative health care data to identify all adult patients undergoing selected elective intermediate- to high-risk noncardiac surgeries in Ontario, Canada between 2009 and 2022. The primary outcome was 30-day mortality following surgery. The secondary outcomes were number of days alive at home, hospital length of stay, total health care system costs, discharge disposition, and readmissions. We used regression models to estimate the adjusted association between the exposure and outcomes. RESULTS: This study identified 562,115 patients, including 41,191 (7.3%) from Northern Ontario. We did not find strong evidence that mortality rates were higher for Northern vs Southern Ontario residents (adjusted odds ratio, 1.04; 95% confidence interval [CI], 0.85 to 1.27). Health system costs were lower for Northern Ontario residents at 30 days [adjusted ratio of mean (RoM), 0.92; 95% CI, 0.89 to 0.96] and at 365 days (adjusted RoM, 0.93; 95% CI, 0.90 to 0.96). Hospital length of stay was longer for Northern Ontario residents (adjusted RoM, 1.06; 95% CI, 1.01 to 1.11). The number of days alive at home and rate of readmission were not statistically different between the two groups. CONCLUSION: Northern Ontario residency was not associated with increased odds of mortality after intermediate- to high-risk elective noncardiac surgery. Overall, we found no clinically meaningful differences in postoperative outcomes between patients from Northern and Southern Ontario.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".