Association Between Endoscopist Annual Procedure Volume and Colonoscopy Quality: Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND & AIMS: In addition to monitoring adverse events (AEs) and post-colonoscopy colorectal cancers (PCCRC), indicators for assessing colonoscopy quality include adenoma detection rate (ADR) and cecal intubation rate (CIR). It is unclear whether there is an association between annual colonoscopy volume and ADR, CIR, AEs, or PCCRC. METHODS: We searched publication databases through March 2019 for studies assessing the relationship between annual colonoscopy volume and outcomes, including ADR, CIR, AEs, or PCCRC. Pooled odds ratios (ORs) were calculated using DerSimonian and Laird random effects models. Sensitivity analyses were performed to assess for potential methodological or clinical factors associated with outcomes. RESULTS: We performed a systematic review of 9235 initial citations, generating 27 retained studies comprising 11,276,244 colonoscopies. There was no association between procedural volume and ADR (OR, 1.00; 95% CI, 0.98-1.02 per additional 100 annual procedures). CIR improved with each additional 100 annual procedures (OR, 1.17; 95% CI, 1.08-1.28). There was a non-significant trend toward decreased overall AEs per additional 100 annual procedures (OR, 0.95; 95% CI, 0.90-1.00). There was considerable heterogeneity among most analyses. CONCLUSIONS: In a systematic review and meta-analysis, we found higher annual colonoscopy volumes to correlate with higher CIR, but not with ADR or PCCRC. Trends toward fewer AEs were associated with higher annual colonoscopy volumes. There are few data available from endoscopists who perform fewer than 100 annual colonoscopies. Studies are needed on extremes in performance volumes to more clearly elucidate associations between colonoscopy volumes and outcomes.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".