Transanal total mesorectal excision for abdominoperineal resection is associated with poor oncological outcomes in rectal cancer patients: A word of caution from a multicentric Canadian cohort study
Bibliographic record
Abstract
AIM: The main objective of this study was to compare the oncological outcomes of patients undergoing abdominoperineal resection (APR) versus low anterior resection (LAR) through a transanal total mesorectal excision (taTME) approach. METHOD: A total of 360 adult patients with a diagnosis of rectal cancer were enrolled at participating centres from the Canadian taTME Expert Collaboration. Forty-three patients received taTME-APR and received 317 taTME-LAR. Demographic, operative, pathological and follow-up data were collected and merged into a single database. Results are presented as hazard ratio (HR) and 95% confidence interval. All analyses were performed in the R environment (v.3.6). RESULTS: The proportion of patients with a positive circumferential radial margin status was higher in the taTME-APR group than the taTME-LAR group (21% vs. 9%, p = 0.001). Complete TME was achieved in 91% of those undergoing APR compared with 96% of those undergoing LAR (p = 0.25). APR was associated with a greater rate of local recurrence relative to LAR, although it was not significant [crude HR = 3.53 (95% CI 0.92-13.53)]. Circumferential margin positivity was significantly associated with a higher rate of systemic recurrence [crude HR = 3.59 (95% CI 1.38-9.3)]. CONCLUSION: Our results demonstrate inferior outcomes in those undergoing taTME-APR compared with taTME-LAR. The use of this technique for this particular indication needs to be carefully considered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".