Endoscopic mucosal resection is effective for laterally spreading lesions at the anorectal junction
Bibliographic record
Abstract
OBJECTIVE: The optimal approach for removing large laterally spreading lesions at the anorectal junction (ARJ-LSLs) is unknown. Endoscopic mucosal resection (EMR) is a definitive therapy for colorectal LSLs. It is unclear whether it is an effective modality for ARJ-LSLs. DESIGN: EMR outcomes for ARJ-LSLs (distal margin of ≤20 mm from the dentate line) in comparison with rectal LSLs (distal margin of >20 mm from the dentate line) were evaluated within a multicentre observational cohort of LSLs of ≥20 mm. Technical success was defined as the removal of all polypoid tissue during index EMR. Safety was evaluated by the frequencies of intraprocedural bleeding, delayed bleeding, deep mural injury (DMI) and delayed perforation. Long-term efficacy was evaluated by the absence of recurrence (either endoscopic or histologic) at surveillance colonoscopy (SC). RESULTS: Between July 2008 and August 2019, 100 ARJ-LSLs and 313 rectal LSLs underwent EMR. ARJ-LSL median size was 40 mm (IQR 35-60 mm). Median follow-up at SC4 was 54 months (IQR 33-83 months). Technical success was 98%. Cancer was present in three (3%). Recurrence occurred in 15.4%, 6.8%, 3.7% and 0% at SC1-SC4, respectively. Among 30 ARJ-LSLs that received margin thermal ablation, no recurrence was identified at SC1 (0.0% vs 25.0%, p=0.002). Technical success, recurrence and adverse events were not different between groups, except for DMI (ARJ-LSLs 0% vs rectal LSLs 4.5%, p=0.027). CONCLUSION: EMR is an effective technique for ARJ-LSLs and should be considered a first-line resection modality for the majority of these lesions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".