A105 ENDOSCOPIC MUCOSAL RESECTION AND ENDOSCOPIC SUBMUCOSAL DISSECTION FOR ILEOCECAL VALVE NEOPLASIA: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background Neoplastic lesions at the ileocecal valve (ICV) represent a complex lesion subgroup given the unique anatomical characteristics of this location. Both endoscopic mucosal resection (EMR) and endoscopic submucosal dissection (ESD) are established techniques for colorectal neoplasia but comparative analyses for ICV lesions are lacking. Aims Evaluate the performance of EMR and ESD for ICV neoplasia. Methods Between Jan 2000 to Aug 2021, two authors independently searched MEDLINE, EMBASE and Cochrane Libraries for relevant citations evaluating the performance of either EMR and/or ESD for ICV neoplasia; defined as lesions involving at least one component of the ICV complex. The rate of technical success (complete removal of all neoplastic tissue during index procedure of those lesions deemed amenable to endoscopic resection), clinically significant post-endoscopic resection bleeding (CSPEB), delayed perforation, and recurrence were assessed. Meta-analysis was performed using a random-effects model. Results Nine studies (367 patients, 252 EMR, 115 ESD) were included in the analysis. Successful removal of all visible neoplastic tissue of those deemed amenable to endoscopic resection was 98.1% (EMR 99.6%, ESD 97.4%). Of note, only 2 studies, both assessing EMR, provided data on lesions which were not considered for endoscopic resection ranging from 5.6–23.7%. Average procedure time ranged from 45–49 minutes for EMR and 52–191 minutes for ESD. Clinically significant post-endoscopic resection bleeding occurred in 6.2% (EMR 9.4%, ESD 4.4%). Delayed perforation occurred in 0.6% (EMR 0.4%, ESD 2.0%). Recurrence occurred in 3.1% (EMR 13.2%, ESD 1.9%). Conclusions Endoscopic resection, both with EMR and ESD, demonstrates high technical success and good adverse event profiles amongst ICV neoplasia deemed amenable for endoscopic resection. Funding Agencies None
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".