Endoscopic mucosal resection using a grasp-and-snare technique
Bibliographic record
Abstract
BACKGROUND AND STUDY AIM: Endoscopic mucosal resection (EMR) is a minimally invasive method for en bloc removal of superficial gastrointestinal lesions. The aim of this study was to evaluate the feasibility of a novel grasp-and-snare EMR technique. METHODS: In 10 domestic pigs, gastric lesions of approximately 3 cm were marked using electrocautery. EMR was performed using a double-channel endoscope. A novel tissue anchor was used through one channel, and a monofilament snare through the other. After submucosal injection, a circumferential mucosal incision was created. The tissue-anchoring device was then advanced through the open snare and anchored into the submucosal layer. The tissue-anchoring device was partly retracted into the endoscope and the snare was positioned into the circular incision. The snare was subsequently closed and the specimen resected by applying high-frequency electrocautery. RESULTS: Mean time to perform EMR was 32.4 minutes (range 22-41 minutes, SD 6.3). EMR yielded specimens that ranged in area from 2.7 cm (minor axis) by 2.8 cm (major axis) to 4.0 cm by 4.2 cm (mean area 9.36 cm(2); range 5.94-13.19 cm(2); SD +/- 2.50). Complete en bloc resection including all electrocautery markings was achieved in 9/10 cases. In one case, resection was achieved in two steps. One gastric wall perforation occurred. No other adverse events were observed. CONCLUSIONS: Grasp-and-snare EMR is feasible in an animal model. The technique can be performed efficiently compared with standard methods. To avoid perforation, caution is needed to ensure that tissue anchor needles are placed within and not deeper than the submucosal layer prior to tissue retraction.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".