Regionalization and Outcomes of Lung Cancer Surgery in Ontario, Canada
Bibliographic record
Abstract
Purpose Regionalization of complex surgery to high-volume hospitals has been advocated based on cross-sectional volume-outcome studies. In April 2007, the agency overseeing cancer care in Ontario, Canada, implemented a policy to regionalize lung cancer surgery at 14 designated hospitals, enforced by economic incentives and penalties. We studied the effects of implementation of this policy. Methods Using administrative health data, we used interrupted time series models to analyze the immediate and delayed effects of implementation of the policy on the distribution of lung cancer surgery among hospitals, surgical outcomes, and health services use. Results From 2004 to 2012, 16,641 patients underwent surgery for lung cancer. The proportion of operations performed in designated hospitals increased from 71% to 89% after the policy was implemented. Although operative mortality decreased from 4.1% to 2.9% (adjusted odds ratio, 0.68; 95% CI, 0.58 to 0.81; P < .001), the reduction was due to a preexisting declining trend in mortality. In contrast, in the years after implementation of the policy, length of hospital stay decreased more than expected from the baseline trend by 7% per year (95% CI, 5% to 9%; P < .001), and the distance traveled by all patients to the hospital for surgery increased by 4% per year (95% CI, 0% to 8%; P = .03), neither of which were explained by preexisting trends. Analyses limited to patients ≥ 70 years of age demonstrated a reduction in operative mortality (odds ratio, 0.80 per year after regionalization; 95% CI, 0.67 to 0.95; P = .01). Conclusion A policy to regionalize lung cancer surgery in Ontario led to increased centralization of surgery services but was not independently associated with improvements in operative mortality. Improvements in length of stay and in operative mortality among elderly patients suggest areas where regionalization may be beneficial.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".