The Canadian Partnership Against Cancer Rectal Cancer Project: Protocol for a Pan-Canadian, Multidisciplinary Quality Improvement Initiative to Optimize the Quality of Rectal Cancer Care
Bibliographic record
Abstract
BACKGROUND: Over the last 2 decades, the use of multimodal strategies, including total mesorectal excision (TME) surgery, preoperative chemotherapy, multidisciplinary case conference, pelvic magnetic resonance imaging, and pathologic assessment using Quirke method, has led to significant improvements in oncologic outcomes for patients with rectal cancer. Although the literature supports claims on the effectiveness of these multimodal strategies, the uptake of these multimodal strategies varies considerably among centers, suggesting that the best evidence is not always implemented into clinical practice. OBJECTIVE: This study aims to perform a quality improvement initiative to (1) identify existing gaps in care for these multimodal strategies and (2) implement knowledge translation (KT) interventions to close these gaps to optimize quality of care for patients with rectal cancer across high-volume centers in Canada. METHODS: Process indicators for the selected multimodal strategies to optimize rectal cancer care will be selected and prospectively collected for all patients with stages 1 to 3 rectal cancer undergoing TME surgery. KT interventions, including audit and feedback, opinion leaders, and community of practice, will be implemented to increase the uptake of these clinical strategies. RESULTS: The uptake of the process indicators over time and the effect of the uptake of the process indicators on short- and long-term oncologic outcomes will be evaluated for each multimodal strategy. CONCLUSIONS: This quality improvement initiative will identify existing gaps in care for the selected multimodal strategies and implement KT interventions to close these gaps. The results of this study will inform further efforts to optimize rectal cancer care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/15535.
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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 | 0.130 | 0.091 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".