Feasibility of Underwater Endoscopic Mucosal Resection for Colorectal Lesions: A Single Center Study in Japan
Bibliographic record
Abstract
Background: Underwater endoscopic mucosal resection (U-EMR) has emerged as an alternative technique for the resection of colorectal lesions. This study aimed to evaluate our initial experience using U-EMR. Methods: This is a single-center, retrospective case series study. We analyzed the clinical outcomes of consecutive patients who underwent U-EMR in our endoscopy center, from December 2015 to February 2017. Results: Our analysis included 64 lesions, contributed by 38 patients, with a mean age of 68.6 years (range, 25 to 90 years). The study sample included 33 right-sided and 25 left-sided colon lesions, and seven rectal lesions, with an average size of 16.2 mm (6 - 40 mm). Of these, 46 lesions were polypoid and 18 ones non-polypoid. Histologically, 31 lesions were low-grade adenomas, eight ones were high-grade adenomas, 11 were mucosal cancers, four were submucosal cancers, and 10 were classified as “others” . En bloc resection was achieved in 52 (81%) lesions, with an en bloc resection rate of 95% for lesions < 20 mm and 55% for lesions < 20 mm. Complete resection of neoplastic epithelial lesions, defined by a negative pathological margin, was achieved in 32 of 59 neoplastic epithelial lesions (54%). We identified three cases (5%) of post-procedural bleeding and one case of perforation (2%). Conclusions: U-EMR can be feasibly used for resection of colonic lesions, including lesions >= 20 mm, although the en bloc resection rate for these lesions was lower than for lesions < 20 mm. Gastroenterol Res. 2018;11(4):274-279 doi: https://doi.org/10.14740/gr1021w
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".