Knowledge Translation Interventions to Address Gaps in Rectal Cancer Care
Bibliographic record
Abstract
Importance: Over the last 2 decades, increasing use of multimodal strategies has led to significant improvements in oncologic outcomes for patients with rectal cancer. However, uptake of these strategies varies among centers, suggesting that best evidence is not always implemented into practice. Objectives: To identify gaps in care and initiate knowledge translation interventions to close existing gaps. Design, Setting, and Participants: This 3-year multifaceted, prospective quality improvement study was conducted at 8 high-volume rectal cancer centers across Canada. From April 2016 to December 2018, patients with stage I to III rectal cancer undergoing total mesorectal excision were enrolled. Data were analyzed from January 2022 through December 2023. Interventions: Process measures for multimodal strategies to optimize rectal cancer care were selected and prospectively collected for patients with stage I to III rectal cancer undergoing total mesorectal excision. Knowledge translation interventions were implemented to increase uptake of these strategies. Main Outcome and Measure: Change in uptake of process measures over the study period, with measures taken every 3 months, from time 1 (baseline) to time 7 (18 months). Results: Among 645 patients with stage I to III rectal cancer (389 male [60.3%]; mean [SD] age, 68.1 [8.2] years), iterative results showed that uptake of 6 of 12 process measures (eg, presentation at multidisciplinary cancer conference: 22 of 77 patients [28.6%] at time 1 to 64 of 91 patients [70.3%] at time 7; P < .001) and 1 pathology measure (inadequate lymph node retrieval: 15 of 77 patients at time 1 [19.5%] to 6 of 91 patients at time 7 [6.6%]; P = .002) improved over time. Positive circumferential resection margin, positive distal margin, and inadequate lymph node retrieval rates at 2 years were 44 patients (6.8%), 10 patients (1.6%), and 79 patients (12.2%), respectively. Conclusions and Relevance: In this study, there was an improvement in 6 process measures and 1 pathology measure for patients with stage I to III rectal cancer. Furthermore, this study led to standardized processes of care for rectal cancer that may facilitate continuous quality improvement and multicenter trials across Canada.
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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 | 0.038 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".