Impact of EndoRings on colon adenoma detection rate: A meta‐analysis of randomized trials
Bibliographic record
Abstract
BACKGROUND AND AIM: Evidence of a superior efficacy of EndoRings over standard colonoscopy in improving colon adenoma detection rate is lacking. We aimed to compare EndoRings and standard colonoscopy through a pairwise meta-analysis of randomized trials. METHODS: We searched the PubMed/Medline and Embase database through July 2020 and identified five randomized controlled trials (recruiting 2751 patients). The primary outcome was adenoma detection rate; secondary outcomes included advanced and sessile serrated adenoma detection rate, mean adenoma per colonoscopy, cecal intubation rate, and time. We performed pairwise meta-analysis through a random-effects model and expressed data as risk ratio and 95% confidence interval. RESULTS: Overall, pooled adenoma detection rate was 53.9% (49-58.8%) with EndoRings and 49.1% (42-56.1%) with standard colonoscopy (risk ratio 1.05, 0.95-1.17). Advanced adenoma detection (risk ratio 0.91, 0.74-1.12), sessile serrated detection rate (risk ratio 1.10, 0.81-1.50), and polyp detection rate (risk ratio 1.06, 0.98-1.15) were similar between the two groups. Likewise, mean adenoma per colonoscopy (mean difference 0.17, -0.09 to 0.43), cecal intubation rate (risk ratio 1.00, 0.99-1.01), and cecal intubation time (mean difference 0.20 min, -0.34 to 0.74) did not differ between the two add-on devices. No serious adverse event was observed. CONCLUSION: EndoRings did not seem to significantly improve the diagnostic performance of colonoscopy. Further trials are needed to confirm these results.
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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 | 0.028 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".