Early Surveillance Endoscopy Should Be Performed Selectively After Transanal Endoscopic Microsurgery for Rectal Lesions
Bibliographic record
Abstract
Introduction Local recurrence (LR) rates after transanal endoscopic microsurgery (TEM) are unclear, and the utility of early postoperative surveillance for low-risk lesions is unknown. This study aimed to define LR after TEM for benign polyps and invasive adenocarcinoma, describe risk factors for LR, and evaluate the utility of early surveillance endoscopy. Methods This retrospective cohort study was conducted at two hospitals in Winnipeg, Manitoba, Canada. Adult patients who underwent TEM between 2009 and 2020 were evaluated for inclusion. The primary outcome was the rate of LR on surveillance endoscopy. Other outcomes included risk factors for LR and diagnostic yield of surveillance endoscopy. Results Among 357 patients who underwent TEM for benign polyps, LR was 10.5% (95% confidence interval (CI) 5.8-15.2) at three years. Positive margin was correlated with LR on multivariate analysis (hazard ratio (HR) 8.01, 95% CI 2.78-23.08). TEM defect closure was associated with lower LR on multivariate analysis (HR 0.19, 95% CI 0.06-0.59). Among 124 patients who underwent TEM for rectal adenocarcinoma, LR was 15.0% (95% CI 6.0-24.0) at three years. The first surveillance endoscopy had a 1.4% yield for low-risk patients (benign lesion, negative margins, and closed TEM defect) and 6.9% for all others. Conclusions LR at three years after TEM was 10.5% for benign polyps and 15.0% for adenocarcinomas. Early surveillance endoscopy can be considered low yield in some patients after TEM, which can be informative for shared decision-making regarding whether to proceed with early endoscopy in a low-risk subgroup of patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".