Colorectal Cancer Surgery Quality in Manitoba: A Population-Based Descriptive Analysis
Bibliographic record
Abstract
Unwarranted clinical variation in healthcare impacts access, productivity, performance, and outcomes. A strategy proposed for reducing unwarranted clinical variation is to ensure that population-based data describing the current state of health care services are available to clinicians and healthcare decision-makers. The objective of this study was to measure variation in colorectal cancer surgical treatment patterns and surgical quality in Manitoba and identify areas for improvement. This descriptive study included individuals aged 20 years or older who were diagnosed with invasive cancer (adenocarcinoma) of the colon or rectum between 1 January 2010 and 31 December 2014. Laparoscopic surgery was higher in colon cancer (24.1%) compared to rectal cancer (13.6%). For colon cancer, the percentage of laparoscopic surgery ranged from 12.9% to 29.2%, with significant differences by regional health authority (RHA) of surgery. In 86.1% of colon cancers, ≥12 lymph nodes were removed. In Manitoba, the negative circumferential resection margin for rectal cancers was 96.9%, and ranged from 96.0% to 100.0% between RHAs. The median time between first colonoscopy and resection was 40 days for individuals with colon cancer. This study showed that high-quality colorectal cancer surgery is being conducted in Manitoba along with some variation and gaps in quality. As a result of this work, a formal structure for ongoing measuring and reporting surgical quality has been established in Manitoba. Quality improvement initiatives have been implemented based on these findings and periodic assessments of colorectal cancer surgery quality will continue.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".