Endoscopy Unit Level Interventions to Improve Adenoma Detection Rate: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND & AIMS: Adenoma detection rate (ADR) is inversely correlated with the risk of interval colon cancer and is a key target for quality improvement in endoscopy units. We conducted a systematic review and meta-analysis to identify and evaluate the effectiveness of interventions that can be implemented at the endoscopy unit level to improve ADRs. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search was conducted in MEDLINE, Embase, and Cochrane Central Register of Controlled Trials databases between January 1990 and December 2022 to identify relevant studies. Both randomized controlled trials and observational studies were eligible. Data for the primary outcome of ADR were analyzed and reported on the log-odds scale with 95% CIs using a random-effects meta-analysis model using the empiric Bayes estimator. RESULTS: From 10,778 initial citations, 34 studies were included in the meta-analysis comprising 371,041 procedures and 1501 endoscopists. The provision of report cards (odds ratio [OR], 1.28; 95% CI, 1.13-1.45; P < .001) and the presence of an additional observer to identify polyps (OR, 1.25; 95% CI, 1.09-1.43; P = .002) were associated with significant increases in ADRs whereas multimodal interventions were borderline significant (OR, 1.18; 95% CI, 1.00-1.40; P = .05) and withdrawal time monitoring was not associated significantly with an increase in ADRs (OR, 1.35; 95% CI, 0.93-1.96; P = .11). CONCLUSIONS: The provision of report cards and the presence of an additional observer to identify polyps are associated with improved ADRs and should be considered for implementation in endoscopy facilities.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.045 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".