High- versus low-intensity knowledge translation interventions for surgeons and rates of local tumour recurrence after rectal cancer surgery: an Ontario study
Bibliographic record
Abstract
Background Given that diagnostic, neoadjuvant treatment, and surgical approaches to rectal cancer have changed markedly in the last 25 years, knowledge translation (KT) may be useful to optimize rectal cancer surgery and improve patient outcomes. We sought to evaluate the impact of surgeon-directed KT to improve the quality of rectal cancer surgery on local tumour recurrence in Ontario. Methods Ontario’s 14 health regions were previously categorized into 2 high-intensity and 12 low-intensity KT regions, based on KT methods (e.g., theory, audit, feedback), applied from 2006 to 2012 to improve the quality of rectal cancer surgery. In the high-intensity regions, efforts encouraged preoperative magnetic resonance imaging, appropriate radiation, and optimal surgical technique. We abstracted hospital chart data from across Ontario for a random sample of cases from 2010 to 2012 based on the respective population of a region and the relative hospital case volume within their region. The main study outcome was local tumour recurrence. Results In the high-intensity and low-intensity KT regions, we reviewed data from 523 (48.6%) and 557 (51.4%) patients, respectively. Descriptive variables (e.g., age, sex, tumour stage) were similar between groups. In the high- and low-intensity regions, the proportion of patients with a permanent stoma was 31.4% and 26.4% (p = 0.08), the proportion with positive radial margins was 8.0% and 6.1% (p = 0.2), and the proportion with local tumour recurrence was 6.3% and 5.2% (p = 0.2), respectively. The adjusted risk of time to local recurrence was similar in the high- and low-intensity KT regions (hazard ratio 0.72, 95% confidence interval 0.50–1.05). Conclusion The use of resource-intense methods was not associated with improved patient outcomes, including local tumour recurrence. New approaches are needed to optimize the population-level quality of rectal cancer surgery.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".