Best practices for flexible endoscope high-level disinfection – An integrative review
Bibliographic record
Abstract
Background: Flexible endoscopes have a complicated design which includes several small lumen channels intended to facilitate the flow of fluids, tissue, and tools through the length of the device. This complex design leads to reprocessing challenges for high-level disinfection (HLD) to ensure endoscopes are free from contaminants that could lead to hospital-acquired infections. The aim of this project was to identify optimal strategies and obstacles for each stage of flexible endoscope HLD through an integrative review with the goal of achieving reprocessing excellence. Methods: A literature search was conducted using PubMed/Medline and CINAHL databases. A total of 32 articles and six guidelines were included in the review. Results: Ten elements with best-practice recommendations of flexible endoscope HLD have been identified. The HLD elements that received the most literature support include quality assurance/process monitoring and manual cleaning/decontamination. Several barriers to the adequate performance of HLD elements were also identified. Conclusion: This integrative review applied varying levels of rigour to identify and synthesize best practices for the following HLD elements: point-of-use treatment, transport, leak testing, manual cleaning/decontamination, visual inspection, manual or automated HLD, rinsing/drying, storage/hang time, record keeping, and quality assurance/process monitoring.
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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".