Retrospective review of rectal cancer surgery in northern Alberta
Bibliographic record
Abstract
INTRODUCTION: Previous studies, including research published more than 10 years ago in Northern Alberta, have demonstrated improved outcomes with increased surgical volume and subspecialisation in the treatment of rectal cancer. We sought to examine contemporary rectal cancer care in the same region to determine whether practice patterns have changed and whether outcomes have improved. METHODS: We reviewed the charts of all patients with rectal adenocarcinoma diagnosed between 1998 and 2003 who had a potentially curative resection. The main outcomes examined were 5-year local recurrence (LR) and disease-specific survival (DSS). Surgeons were classified into 3 groups according to training and volume, and we compared outcome measures among them. We also compared our results to those of the previous study from our region. RESULTS: We included 433 cases in the study. Subspecialty-trained colorectal surgeons performed 35% of all surgeries in our study compared to 16% in the previous study. The overall 5-year LR rate and DSS in our study were improved compared to the previous study. On multivariate analysis, the only factor associated with increased 5-year LR was presence of obstruction, and the factors associated with decreased 5-year DSS were high-volume noncolorectal surgeons, presence of obstruction and increased stage. CONCLUSION: Over the past 10 years, the long-term outcomes of treatment for rectal cancer have improved. We found that surgical subspecialization was associated with improved DSS but not LR. Increased surgical volume was not associated with LR or DSS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".