Safety and efficacy of endoscopic submucosal dissection for rectal neoplasia: a multicenter North American experience
Bibliographic record
Abstract
Abstract Background and aims Rectal lesions traditionally represent the first lesions approached during endoscopic submucosal dissection (ESD) training in the West. We evaluated the safety and efficacy of rectal ESD in North America. Methods This is a multicenter retrospective analysis of rectal ESD between January 2010 and September 2018 in 15 centers. End points included: rates of en bloc resection, R0 resection, adverse events, comparison of pre- and post-ESD histology, and factors associated with failed resection. Results In total, 171 patients (median age 63 years; 56 % men) underwent rectal ESD (median size 43 mm). En bloc resection was achieved in 141 cases (82.5 %; 95 %CI 76.8–88.2), including 24 of 27 (88.9 %) with prior failed endoscopic mucosal resection (EMR). R0 resection rate was 74.9 % (95 %CI 68.4–81.4). Post-ESD bleeding and perforation occurred in 4 (2.3 %) and 7 (4.1 %), respectively. Covert submucosal invasive cancer (SMIC) was identified in 8.6 % of post-ESD specimens. There was one case (1/120; 0.8 %) of recurrence at a median follow-up of 31 weeks; IQR: 19–76 weeks). Older age and higher body mass index (BMI) were predictors of failed R0 resection, whereas submucosal fibrosis was associated with a higher likelihood of both failed en bloc and R0 resection. Conclusion Rectal ESD in North America is safe and is associated with high en bloc and R0 resection rates. The presence of submucosal fibrosis was the main predictor of failed en bloc and R0 resection. ESD can be considered for select rectal lesions, and serves not only to establish a definitive tissue diagnosis but also to provide curative resection for lesions with covert advanced disease.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".