Assessment of endoscope reprocessing at World Gastroenterology Organisation training centers using adenosine triphosphate testing
Bibliographic record
Abstract
Abstract Background and study aims Adequacy of endoscope disinfection in resource-limited settings is unknown. Adenosine triphosphate (ATP) testing is useful for evaluation of endoscope reprocessing, and ATP <200 relative light units (RLUs) after manual endoscope cleaning has been associated with adequacy of endoscope disinfection. Methods Consecutive endoscopes undergoing reprocessing at five World Gastroenterology Organisation (WGO) training centers underwent ATP testing before and after an on-site educational intervention designed to optimize reprocessing practices. Results A total of 343 reprocessing cycles of 65 endoscopes were studied. Mean endoscope age was 5.3 years (range 1–13 years). Educational interventions, based on direct observation of endoscope reprocessing practices at each site, included refinements in pre-cleaning, manual cleaning, high-level disinfection, and endoscope drying and storage. The percentage of reprocessing cycles with post-manual cleaning ATP ≧200 decreased from 21.4% prior to educational intervention to 14.8% post-intervention (P=0.11). In multivariable logistic modelling, gastroscopes were significantly less likely (odds ratio [OR] 0.04, 95% confidence interval [CI] 0.01–0.19; P<0.001) than colonoscopes to achieve post-manual cleaning ATP < 200. No other factor (educational intervention, study site, endoscope age) was significantly associated with improved outcomes. Endoscope ID was not significantly associated with ATP values, and sites that performed manual versus automated HLD did not have significantly different likelihood of post-manual cleaning ATP <200 (OR 1.18, 95% CI 0.56–2.50; P=0.67). Conclusions In resource-limited settings, approximately 20% of endoscope reprocessing cycles may result in inadequate disinfection. This was not significantly improved by a comprehensive educational intervention. Alternative approaches to endoscope reprocessing are needed.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".