Recurrence following transanal total mesorectal excision for rectal cancer: a monocentric retrospective series of technically difficult cases
Bibliographic record
Abstract
PURPOSE: Transanal total mesorectal excision (TaTME) has been proposed to overcome surgical difficulties encountered during rectal resection, especially for patients having high body mass index or low rectal cancer. The aim of this study was to evaluate oncologic outcomes following TaTME. METHODS: This retrospective study included all consecutive patients with rectal cancer who had a TaTME from 2013 to 2019. The main outcome was the incidence of locoregional recurrence by the end of the follow-up period. RESULTS: Among a total of 81 patients, 96.3% were male, and their mean age was 63±9 years. The mean body mass index was 30.3±5.7 kg/m2, and the median distance from tumor to anal verge was 5.0 cm (interquartile range [IQR], 4.0-6.0 cm). Most patients had a low anterior resection performed (n=80, 98.8%) with a diverting ileostomy (n=64, 79.0%). Distal and circumferential resection margins were positive in 2.5% and 6.2% of patients, respectively. Total mesorectal excision was complete or near complete in 95.1% of patients. A successful resection was achieved in 72 patients (88.9%). After a median follow-up of 27.5 months (IQR, 16.7-48.1 months), 4 patients (4.9%) experienced locoregional recurrence. Anastomotic leaks were observed in 21 patients (25.9%). At the end of the follow-up, 69 patients (85.2%) were stoma-free. CONCLUSION: TaTME was associated with acceptable oncological outcomes, including low locoregional recurrence rates in selected patients with low rectal cancer. Although associated with a high incidence of postoperative morbidities, the use of TaTME enabled a high rate of successful sphincter-saving procedures in selected patients who posed a technical challenge.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".