Endocuff Vision improves adenoma detection rate in a large screening-related cohort
Bibliographic record
Abstract
Abstract Background and study aims Endocuff Vision (ECV) increases adenoma detection rate (ADR) in randomized clinical trials; however, observational effectiveness data are lacking. We evaluated the effectiveness, safety, and practical aspects of ECV use in a large screening-related real-world cohort. Patients and methods In this observational study, patients undergoing screening-related colonoscopy from November 2018 to April 2019 comprised the baseline period, and those undergoing it from June to November 2019 comprised the ECV period, where ECV use was discretionary. The primary outcome was ADR, compared: 1) between ECV use and standard colonoscopy across both periods; and 2) between time periods. Secondary outcomes included indication-specific ADR, sessile serrated ADR (SSADR), cecal intubation rate (CIR), procedure times, patient comfort scores, and sedation use. Multilevel logistic regression was performed, yielding adjusted odds ratios (AOR) with 95 % confidence intervals (CIs). Results In 15,814 colonoscopies across both time periods, ADR was 46.7 % with standard colonoscopy and 54.6 % when ECV was used (P < 0.001). Endoscopists used ECV in 77.6 % of procedures in the ECV period, during which overall ADR rose to 53.2 % compared to 46.3 % in the baseline period (P < 0.001). ECV use was significantly associated with higher ADR (AOR 1.24, 95 % CI 1.10 to 1.40) after adjusting for relevant covariates including time period. ECV use did not result in lower CIR, longer procedure time, increased sedation use, or poorer comfort scores. Conclusions ECV use is associated with improved ADR without negatively impacting other key procedure and patient-related factors. Future studies should evaluate the cost-effectiveness of incorporating ECV into routine screening-related practice.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".