A65 ENDOSCOPIST-TARGETED INTERVENTIONS TO OPTIMIZE ADENOMA DETECTION RATE - A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background Adenoma detection rate (ADR) has emerged as the strongest quality assurance metric that has consistently been shown to be inversely associated with the development of colorectal cancer after colonoscopy. Unfortunately, marked variability in ADR exists among endoscopists. A multitude of interventions targeted at endoscopists to optimize their ADR have been reported, including but not limited to withdrawal time, in room observers, physician report cards, and quality improvement and training programs. However, it is unclear which of them are truly effective. Aims We performed a systematic review and meta-analysis of the literature to evaluate the effectiveness of endoscopist-targeted interventions to improve adenoma detection rate (ADR) or polyp detection rate (PDR). Methods Systematic searches of major databases were conducted through to March 2018 to identify potentially relevant studies. Both randomized controlled trials and observational studies were included. Data for ADR and PDR were analyzed on the log-odds scale using a random-effects meta-analysis model using restricted maximum likelihood (with Mantel-Haenszel fixed-effect meta-analysis used for fewer than 4 studies). Statistical effect-size heterogeneity was assessed using a Chi2 test and quantifying the relative proportion of variation using the I2 statistic. Publication bias was assessed by the Harbord regression test. Results From 4299 initial studies, 24 were included in the systematic review and 13 were included in the meta-analysis representing a total of 55,090 colonoscopies. Physician report card interventions (7 studies) and withdrawal time focused interventions (6 studies) were meta-analyzed. The pooled odds ratio for ADR for report card interventions was 1.31 (95% CI: 1.15, 1.50; p<0.0001), favoring report cards to detect more adenomas. Statistical heterogeneity was detected with substantial relative effect-size variability (Chi2, p<0.0001; I2=80.1%). No statistical evidence of publication bias was found. 6 studies reported data for PDR using withdrawal time focused interventions, with 3 of these reporting data on ADR. The pooled odds ratio for ADR was 1.02 (95% CI: 0.86, 1.22; p=0.81) and for PDR was 1.07 (95% CI: 0.88, 1.31; p=0.51) which were not statistically significant. Statistical heterogeneity was detected in both groups (Chi2, p<0.001; I2=82.2% for ADR and I2=89.4% for PDR) and there was statistical evidence of publication bias. Figures 1 and 2 represent Forest plots for the effect of pre-and post-report card and withdrawal time focused interventions on ADR. Conclusions Our study provides evidence that the distribution of colonoscopy quality report cards to physicians significantly improves overall ADR and should strongly be considered as part of quality improvement programs aimed at optimizing colonoscopy performance. 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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".