A121 UNDERWATER EMR FOR NON-LIFTING SESSILE COLORECTAL LESIONS
Bibliographic record
Abstract
Abstract Background Sessile colorectal lesions which do not elevate with submucosal injection — “non-lifting” lesions — are considered poor candidates for EMR due to concerns of possible invasive cancer and increased procedural risk. However, a non-lifting sign is an unreliable predictor of malignancy, relegating many benign lesions to surgical resection. Underwater EMR (UEMR), which obviates submucosal injection, is effective for sessile colorectal polyps but has not been evaluated specifically for non-lifting lesions. Aims The aim of this study was to assess the efficacy of UEMR for “non-lifting” large sessile colorectal lesions with the hypothesis that UEMR may have a clinical role in managing complex lesions. Methods We reviewed our database from 2016 to 2019 for patients referred for large (≥ 20 mm) non-lifting colorectal lesions without overt signs of invasive cancer, who subsequently underwent UEMR. Results Thirty-two cases were successfully treated with single session UEMR. 18 (56%) were de novo lesions whereas the remainder had undergone previous attempt(s) at conventional EMR. The mean lesion size was 37 ± 17 mm. 4 cases (13%) were resected en bloc; the remainder piecemeal. Final pathology was T1 adenocarcinoma, N=3 (9%); tubulovillous adenoma, N=15 (47%); tubular adenoma, N=8 (25%); sessile serrated, N=6 (19%); high-grade dysplasia, N=2 (6%). One patient with cancer underwent surgical resection (T1N0); the remainder had endoscopic follow-up over 8 ± 3 months with benign recurrent/residual lesions in 8%, all amenable to UEMR. There were no procedural complications. Conclusions In this series of large sessile non-lifting colorectal lesions, UEMR was effective for both de novo and previously treated lesions, obviating surgery in the majority of cases. Funding Agencies None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".