Monitoring excess unplanned return to theatre following colorectal cancer surgery
Bibliographic record
Abstract
BACKGROUND: To develop a risk-adjustment model for unplanned return to theatre (URTT) outcomes following colorectal surgeries in Australia and New Zealand hospitals and apply top-down and bottom-up statistical process control methods for fair comparison of hospitals and surgeons' URTT rates. METHODS: We analysed URTT outcomes from hospitals contributing data to the Bi-National Colorectal Cancer Audit clinical registry between 2007 and 2016. Preoperative and intraoperative covariates were considered for risk adjustment. A risk-adjusted rate funnel plot was prepared for between-hospital comparisons and identification of outlying hospitals with unusually high rates of URTT. Cumulative observed-minus-expected charts with cumulative sum signals were then presented for surgeons within an outlying hospital. RESULTS: The study included 15 134 patients and 166 surgeons across 70 hospitals. The weighted average URTT rate was 5.2%. The risk-adjustment model identified 12 preoperative and intraoperative variables that significantly raise the risk of URTT: male sex, American Society of Anesthesiologists score, emergency admissions, conversion entry, left hemicolectomy, total colectomy, proctocolectomy, lower anterior resection, ultra-low anterior resection, abdominoperineal resection, organ resection and excess lymph nodes harvested. Right hemicolectomy significantly reduced risk of URTT. URTT rates were not found to significantly vary across seniority of operator; however, comparisons were limited by lack of data on junior operators. The funnel plot identified five hospitals as 'possible outliers' and hospital T was identified as a 'definite outlier'. The cumulative observed-minus-expected charts with cumulative sum signals showed that within hospital T, one surgeon among three had a particularly bad run of URTTs. CONCLUSION: Feedback from aggregated URTT outcomes using a risk-adjusted rate funnel plot is enhanced when follow-up examination of outlying hospitals is conducted with concurrent application of cumulative observed-minus-expected charts with cumulative sum signals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".