Regional variance in treatment and outcomes of locally invasive (<scp>T4</scp>) rectal cancer in Australia and New Zealand: analysis of the <scp>Bi‐National</scp> Colorectal Cancer Audit
Bibliographic record
Abstract
BACKGROUNDS: Locally invasive T4 rectal cancer often requires neoadjuvant treatment followed by multi-visceral surgery to achieve a radical resection (R0), and referral to a specialized exenteration quaternary centre is typically recommended. The aim of this study was to explore regional variance in treatment and outcomes of patients with locally advanced rectal cancer in Australia and New Zealand (ANZ). METHODS: Data were collected from the Bi-National Colorectal Cancer Audit (BCCA) database. Rectal cancer patients treated between 2007 and 2019 were divided into six groups based on region (state/country) using patient postcode. A subset analysis of patients with T4 cancer was performed. Primary outcomes were positive circumferential resection margin (CRM+), and positive circumferential and/or distal resection margin (CRM/DRM+). RESULTS: A total of 9385 patients with rectal cancer were identified, with an overall CRM+ rate of 6.4% and CRM/DRM+ rate of 8.6%. There were 1350 patients with T4 rectal cancer (14.4%). For these patients, CRM+ rate was 18.5%, and CRM/DRM+ rate was 24.1%. Significant regional variation in CRM+ (range 13.4-26.0%; p = 0.025) and CRM/DRM+ rates (range 16.1-29.3%; p = 0.005) was identified. In addition, regions with higher CRM+ and CRM/DRM+ rates reported lower rates of multi-visceral resections: range 24.3-26.8%, versus 32.6-37.3% for regions with lower CRM+ and CRM/DRM+ rates (p < 0.0001). CONCLUSION: Positive resection margins and rates of multi-visceral resection vary between the different regions of ANZ. A small subset of patients with T4 rectal cancer are particularly at risk, further supporting the concept of referral to specialized exenteration centres for potentially curative multi-visceral resection.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".