The cluster-randomized Quality Initiative in Rectal Cancer trial: evaluating a quality-improvement strategy in surgery
Bibliographic record
Abstract
BACKGROUND: Following surgery for rectal cancer, two unfortunate outcomes for patients are permanent colostomy and local recurrence of cancer. We tested whether a quality-improvement strategy to change surgical practice would improve these outcomes. METHODS: Sixteen hospitals were cluster-randomized to the intervention (Quality Initiative in Rectal Cancer strategy) or control (normal practice) arm. Consecutive patients with primary rectal cancer were accrued from May 2002 to December 2004. Surgeons at hospitals in the intervention arm could voluntarily participate by attending workshops, using opinion leaders, inviting a study team surgeon to demonstrate optimal techniques of total mesorectal excision, completing postoperative questionnaires, and receiving audits and feedback. Main outcome measures were hospital rates of permanent colostomy and local recurrence of cancer. RESULTS: A total of 56 surgeons (n = 558 patients) participated in the intervention arm and 49 surgeons (n = 457 patients) in the control arm. The median follow-up of patients was 3.6 years. In the intervention arm, 70% of surgeons participated in workshops, 70% in intraoperative demonstrations and 71% in postoperative questionnaires. Surgeons who had an intraoperative demonstration provided care to 86% of the patients in the intervention arm. The rates of permanent colostomy were 39% in the intervention arm and 41% in the control arm (odds ratio [OR] 0.97, 95% confidence interval [CI] 0.63-1.48). The rates of local recurrence were 7% in the intervention arm and 6% in the control arm (OR 1.06, 95% CI 0.68-1.64). INTERPRETATION: Despite good participation by surgeons, the resource-intense quality-improvement strategy did not reduce hospital rates of permanent colostomy or local recurrence compared with usual practice.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".