Transanal <i>versus</i> conventional total mesorectal excision for rectal cancer using the IDEAL framework for implementation
Bibliographic record
Abstract
BACKGROUND: Transanal total mesorectal excision (TaTME) is an innovative technique for distal rectal cancer dissection. It has been shown to have similar short-term outcomes to conventional open and laparoscopic total mesorectal excision (cTME), but recent studies have raised concern about increased morbidity and local recurrence rates. The aim of this study was to assess outcomes after TaTME versus cTME for rectal cancer. METHODS: TaTME was implemented in 2014 using IDEAL principles in a single institution. The institution maintains databases for all patients undergoing rectal cancer surgery. This retrospective review compared data collected from all patients who had TaTME with those from a propensity-matched cohort of patients who underwent cTME. The primary outcome was a composite pathological measure combining margin status and quality of total mesorectal excision (TME). Short-term clinical and survival outcomes were also measured. RESULTS: Propensity matching created 109 matched pairs for analysis. Nine patients (8.3 per cent) undergoing TaTME had positive margins and/or incomplete TME, compared with 11 (10.5 per cent) undergoing cTME (P = 0.65). There were no significant differences in morbidity between the TaTME and cTME groups, including number of anastomotic leaks (13.8 versus 18.3 per cent; P = 0.37). The estimated 3-year local recurrence-free survival rate was 96.3 per cent in both groups (P = 0.39). Estimated 3-year overall (93.6 per cent for TaTME versus 94.5 per cent for cTME; P = 0.09) and disease-free (88.1 versus 76.1 per cent; P = 0.90) survival rates were similar. CONCLUSION: TaTME provided similar outcomes to cTME for rectal cancer with the application of IDEAL principles.
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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 | 0.004 | 0.006 |
| 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) | 0.001 | 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".