Long-Term Impact of Regionalization of Thoracic Oncology Surgery
Bibliographic record
Abstract
BACKGROUND: In 2007, Cancer Care Ontario created Thoracic Surgical Oncology Standards for the delivery of surgery, including lobectomy, esophagectomy, and pneumonectomy. These standards regionalized thoracic surgery into designated centers and mandated physical and human resources. This analysis sought to identify the impact of these standards, hereafter referred to as "regionalization," on outcomes after thoracic oncology surgery in Ontario, Canada. METHODS: This study was a population-level analysis of patients undergoing lobectomy, esophagectomy, or pneumonectomy, and it used multilevel regression models to compare 30- and 90-day mortality and length of stay before, during, and after regionalization. Interrupted time series models were used to assess for an impact of regionalization while controlling for ongoing trends. RESULTS: A total of 22,195 surgical procedures (14,902 lobectomies, 4958 esophagectomies, and 2408 pneumonectomies) were performed within the study period. A total of >99% of cases were performed at a designated center after regionalization. Mean annual volumes per designated center increased after regionalization for lobectomy and esophagectomy and decreased for pneumonectomy. The 30- and 90-day mortality and length of stay improved for lobectomy and esophagectomy over the study period, as did 90-day mortality for pneumonectomy. However, the interrupted time series analysis did not demonstrate any statistically significant effect of regionalization on these outcomes, separate from preexisting trends. CONCLUSIONS: Consistent improvements in mortality and length of stay in thoracic surgical oncology occurred on a provincial level between 2003 and 2020, although this analysis does not attribute these improvements to implementation of Thoracic Surgical Oncology Standards including regionalization.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".