A prospective, multicenter, clinical study of duodenoscope contamination after reprocessing
Bibliographic record
Abstract
Abstract Objective: Several clinical procedures utilize duodenoscopes, which are processed for reuse after the procedures are completed. However, infection outbreaks due to improper duodenoscope processing occur frequently. To address this, we aimed to assess the contamination rates of duodenoscopes after reprocessing in nonoutbreak settings. Design and setting: Prospective study in 16 clinical sites in the United States. Methods: We sampled and cultured reprocessed duodenoscopes following the FDA/CDC/ASM guideline; “Duodenoscope Surveillance Sampling and Culturing – Reducing the Risks of Infection.” High-concern (HC) organisms were those highly associated with disease, including gram-negative rods, Staphylococcus aureus, Staphylococcus lugdunensis , β-hemolytic Streptococcus , Enterococcus spp, and yeasts. We evaluated duodenoscopes with ≥1 CFU of organisms after reprocessing. The reprocessing environments were also sampled and cultured. Results: We assessed 859 newer-model (NM) duodenoscopes (TJF-Q180V) and 850 older-model (OM) duodenoscopes (TJF-160F/VF); of these, 35 NM samples (4.1%) and 56 OM samples (6.6%) were contaminated with HC organisms. We detected and classified the HC organisms as gastrointestinal (45.4%), human origin (16.7%), environmental (24.1%), waterborne (13.0%), and unidentified (0.9%). Conclusions: We detected an overall HC contamination rate of 5.3% in nonoutbreak settings. Although the relationship between endoscopic contamination and the occurrence of infections remains unclear, attempts should continue to be made to further reduce contamination rates. Additional improvements to the manufacturer’s instructions for use, human factors during the reprocessing procedure, ongoing training programs, cleanliness of reprocessing environments, and the design of the distal end of the duodenoscope should be considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".