Experience beyond the learning curve of transanal total mesorectal excision (taTME) and its effect on the incidence of anastomotic leak
Bibliographic record
Abstract
BACKGROUND: The most important advancement in the surgical management of rectal cancer has been the introduction of total mesorectal excision (TME). Technical limitations to approaching mid and distal lesions remain. The recently described transanal TME makes it possible to minimize some of the difficulties by improving access. Anastomotic leak is a persistent concern after colorectal surgery no matter what technique is used. The objective of this study was to explore the impact of experience on the incidence of anastomotic leak after transanal TME. Secondary endpoints were local recurrence and margin status. METHODS: A retrospective cohort study was conducted over a period of 3 years at a tertiary care center in Northern Ontario with high volume of rectal cancer patients. The initial 100 consecutive patients with rectal neoplasia who had transanal TME surgery were included. All cases were performed by a single team. The main outcome assessed was the incidence of anastomotic leak beyond a pre-determined learning curve, as previously established in the literature. For statistical analysis, associations between patient characteristics and outcomes were estimated using ordinary least squares and logistic regression. RESULTS: Six cases of anastomotic leak occurred over the course of the study, the last of which occurred in the 37th patient. Relative to a baseline anastomotic leak rate of 7.8%, cumulative sum (CUSUM) analysis indicated that a 50% improvement in risk occurred at trial 50 of 85 patients that had an anastomosis performed. Two patients developed local recurrence during the study period. No correlation between learning curve and oncologic outcomes was identified. CONCLUSIONS: Proficiency is likely to have a positive effect on the 30-day occurrence of anastomotic leak. Larger studies are required to explore the impact of experience on local recurrence.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".