Analysis of mortality in colorectal surgery in the Bi‐National Colorectal Cancer Audit
Bibliographic record
Abstract
BACKGROUND: In the last decade, there has been a significant increase in interest for public reporting of outcome data and performance comparison across institutions and surgeons. This study aims at comparing postoperative mortality after colorectal cancer surgery across units and individual consultants in Australia and New Zealand using funnel plots. METHODS: The Bi-National Colorectal Cancer Audit database was used. Unadjusted and adjusted funnel plots of inpatient mortality were constructed. Risk adjustment was based upon multivariable logistic regression models using purposeful covariate selection. RESULTS: A total of 10 008 patients undergoing surgery for colorectal cancer from 56 surgical units and 90 consultants were identified. Overall inpatient mortality was 1.51%, corresponding to 1.1% for elective and 3.9% for urgent cases. Logistic regression identified age, American Society of Anesthesiologists score, urgent surgery and open surgery to be independently associated with inpatient mortality. Unadjusted and adjusted funnel plot analysis identified three (5.3%) units exceeding the inner limit and none exceeding the outer limit. Six (6.6%) consultants had inpatient mortality between the upper inner and outer limits and one (1.1%) between the inferior inner and outer limits. Upon adjustment, seven (7.7%) consultants had inpatient mortality between the inner and outer limit. Potential limitations of this study include: residual confounding being responsible for the association of open surgery and mortality; incomplete case-mix adjustment resulting in outlier identification; and bias towards inclusion of larger institutions. CONCLUSION: Mortality figures in Australia and New Zealand are comparable to recently reported international data. The vast majority of units and consultants are performing within the expected boundaries.
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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 | 0.018 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".