Outcomes of rectal cancer treatment in rural Australia and New Zealand: analysis of the bowel cancer outcomes registry
Bibliographic record
Abstract
BACKGROUND: The demographics and geography of Australia and New Zealand (ANZ), with few metropolitan centres and vast, sparsely populated rural areas, represent a challenge to providing equal care to all patients. This study aimed to compare rectal cancer care at rural and urban hospitals in ANZ. METHODS: From the Bowel Cancer Outcomes Registry (BCOR, formerly known as the Bi-National Colorectal Cancer Audit; BCCA), rectal cancer patients treated between 2007 and 2020 were compared based on hospital location (urban versus rural). Propensity-score matching was performed to correct for differences in baseline characteristics between groups. RESULTS: A total of 9385 rectal cancer patients were identified from the BCOR: 1329 (14.2%) were treated at rural hospitals and 8056 (85.8%) at urban hospitals. Propensity-score matching resulted in 889 patients in each group, matched for age, ASA score, hospital type (public/private), tumour height from the anal verge, and pre-treatment cT- and cAJCC-stage. Rural patients had fewer pre-treatment MRIs (67.9% versus 74.7%; P = 0.002), and underwent less neoadjuvant therapy (44.7% versus 50.9%; P = 0.01). Rural patients underwent fewer ULARs (39.4% versus 45.6%; P = 0.03), and fewer anastomoses were formed (67.9% versus 74.4%; P = 0.05). CRM rates and postoperative AJCC stages (P = 0.19) were similar between groups (P = 0.87). Fewer rural patients received adjuvant chemotherapy (37.8% versus 43.3%; P = 0.02). CONCLUSION: There are significant differences in pre-treatment MRI rates, (neo)adjuvant treatment rates and surgical procedures performed between rectal cancer patients treated at rural and urban hospitals in ANZ, while CRM rates and postoperative AJCC stages are similar.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".