IDDF2021-ABS-0119 Thermal ablation of mucosal defect margins after endoscopic mucosal resection reduces adenoma recurrence: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Resection of colorectal lesions larger than 20mm is complex and requires advanced endoscopic techniques such as endoscopic mucosal resection (EMR). Adenoma recurrence is a limiting factor especially due to micro-adenomas at the margin of the EMR mucosal defect site. This systematic review and meta-analysis aimed to determine the efficacy of thermal ablation of mucosal defect margins after EMR in reducing adenoma recurrence. Methods A comprehensive, computerized literature search from the PubMed Central, Embase, Cochrane Library, and OVID was performed with the following search terms: coagulation, mucosal defect margin, endoscopic mucosal resection, and adenoma recurrence. Three cohort studies were selected and validated using the Newcastle-Ottawa criteria. Pooled data were combined under a random-effects model. The Cochrane Review Manager Software version 5.3 was used for all analyses. Results Three cohort studies comprising of 361 patients were analyzed. In the random-effects model, the pooled odds ratio (OR) of adenoma recurrence was 0.22 (95% CI 0.13-0.39; I2 = 0%)( IDDF2021-ABS-0119. Figure 1). The pooled data of the three studies showed a trend towards a beneficial effect of thermal ablation of mucosal defect post-endoscopic mucosal resection in reducing the risk of adenoma recurrence. Conclusions Thermal ablation of the mucosal defect margins was shown to have a decreased risk of adenoma recurrence after endoscopic mucosal resection. However, further prospective randomized studies are recommended to confirm this relationship.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".