Underwater vs conventional endoscopic mucosal resection in the management of colorectal polyps: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Recently, underwater endoscopic mucosal resection (UEMR) has shown promising results in the management of colorectal polyps. Some studies have shown better outcomes compared to conventional endoscopic mucosal resection (EMR). We conducted this systematic review and meta-analysis to compare UEMR and EMR in the management of colorectal polyps. Methods We searched several databases from inception to November 2019 to identify studies comparing UEMR and EMR. Outcomes assessed included rates of en bloc resection, complete macroscopic resection, recurrent/residual polyps on follow-up colonoscopy, complete resection confirmed by histology and adverse events. Pooled risk ratios (RR) with 95 % confidence interval were calculated using a fixed effect model. Heterogeneity was assessed by I2 statistic. Funnel plots and Egger’s test were used to assess publication bias. We used the Newcastle-Ottawa scale (NOS) for assessment of quality of observational studies, and the Cochrane tool for assessing risk of bias for RCTs Results Seven studies with 1291 patients were included; two were randomized controlled trials and five were observational. UEMR demonstrated statistically significantly better efficacy in rates of en bloc resection, pooled RR 1.16 (1.08, 1.26), complete macroscopic resection, pooled RR 1.28 (1.18, 1.39), recurrent/residual polyps; pooled RR 0.26 (0.12, 0.56) and complete resection confirmed by histology; pooled RR 0.75 (0.57, 0.98). There was no significant difference in adverse events (AEs); pooled RR 0.68 (0.44, 1.05). Conclusions This meta-analysis found statistically significantly better rates of en bloc resection, complete macroscopic resection, and lower risk of recurrent/residual polyps with UEMR compared to EMR. We found no significant difference in AEs between the two techniques.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.003 | 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".