Educational interventions are associated with improvements in colonoscopy quality indicators: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background and study aims The quality of screening-related colonoscopy depends on several physician- and patient-related factors. Adenoma detection rate (ADR) varies considerably between endoscopists. Educational interventions aim to improve endoscopists’ ADRs, but their overall impact is uncertain. We aimed to assess whether there is an association between educational interventions and colonoscopy quality indicators. Methods A comprehensive search was performed through August 2019 for studies reporting any associations between educational interventions and any colonoscopy quality indicators. Our primary outcome of interest was ADR. Two authors assessed eligibility criteria and extracted data independently. Risk of bias was also assessed for included studies. Pooled rate ratios (RR) with 95 % confidence intervals (CI) were reported using DerSimonian and Laird random effects models. Results From 2,253 initial studies, eight were included in the meta-analysis for ADR, representing 86,008 colonoscopies. Educational interventions were associated with improvements in overall ADR (RR 1.29, 95 % CI 1.25 to 1.42, 95 % prediction interval 1.09 to 1.53) and proximal ADR (RR 1.39, 95 % CI 1.29 to 1.48), with borderline increases in withdrawal time, ([WT], mean difference 0.29 minutes, 95 % CI – 0.12 to 0.70 minutes). Educational interventions did not affect cecal intubation rate ([CIR], RR 1.01, 95 % CI 1.00 to 1.01). Heterogeneity was considerable across many of the analyses. Conclusions Educational interventions are associated with significant improvements in ADR, in particular, proximal ADR, and are not associated with improvements in WT or CIR. Educational interventions should be considered an important option in quality improvement programs aiming to optimize the performance of screening-related colonoscopy.
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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.006 | 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) | 0.004 | 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".