The implementation of a transanal endoscopic microsurgery programme: initial experience with surgical performance
Bibliographic record
Abstract
Abstract Aim Despite transanal endoscopic microsurgery ( TEM ) being used for over 30 years, there has been slow adoption of this modality in many centres. There remains a paucity of research regarding the learning curve and early performance of surgeons who begin to offer TEM . We sought to determine predictors of longer rates of tumour excision and improvements in operative time in a newly established TEM programme. Method All patients who underwent TEM at the Ottawa Hospital, Ottawa, Canada, between October 2009 and September 2014 were included. Data were abstracted through a retrospective chart review. The average rate of lesion excision ( ARE ) was calculated to standardize the operation time by size of the pathological specimen (min/cm 3 ), representing a measure of surgical efficiency. Surgical efficiency was plotted using restricted cubic splines. Predictors of higher ARE were determined using multivariable regression. Results During the study period 108 patients underwent TEM . ARE was available for 95 patients of mean age 67.2 years. The mean ARE was 18.6 min/cm 3 . On adjusting for important covariates, the ARE improved with each additional case until 16 cases were completed. Significant predictors of higher ARE on multivariable analysis were age < 50 years, experience of fewer than five cases, and carcinoid/gastrointestinal stromal tumour or scar histology. Conclusion Operative efficiency appears to improve as surgeons completed 16 TEM cases. We have identified important factors that result in longer operating time. The study has important implications with regard to surgical training and operative planning for new TEM programmes.
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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 | 0.007 |
| 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 | 0.001 |
| 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".