How to clean a catheter: Development of an intervention for intermittent catheter reuse
Bibliographic record
Abstract
Background and Objectives: Much intermittent catheterisation (IC) is carried out using single-use catheters. Waste and costs could be reduced by cleaning and reusing catheters, but is it safe to do so? To answer these questions of safety and sustainability, clinical trials are needed. In this study, we developed a user-tested catheter cleaning method and training materials for use in a clinical trial. Methods: Focus groups selected candidate cleaning methods and developed draft instructions. Users then home tested these methods on uncoated, plastic-based catheters, which were cleaned and reused up to 28 times. Reused and cleaned catheters were analysed using advanced microbiological analysis methods. The refined cleaning method was further tested by a naïve user panel. Additionally, a silicone catheter designed for reuse was tested in the laboratory and for user acceptability. User panel feedback was gathered throughout testing and thematically analysed. Results: Twenty-six IC users were recruited to three user panels. Focus groups identified soap and water (SW) and soap and water plus a 15-minute soak in a chlorine-based cleaning solution (SW-Cl) as the preferred cleaning methods. User testing (≤3 reuses) and laboratory analysis showed SW alone to be less effective than SW-Cl: bacteria were detected in 23/120 (19%) male and 56/108 (52%) female SW samples versus 16/228 (7%) and 16/201 (8%) for SW-Cl. Bacteria were detected in only 1/240 (<0.5%) of catheter samples after 8-≥28 reuses with the SW-Cl method. Naïve user panel results were similar. The silicone catheter was acceptable to users and had comparable laboratory results using SW-Cl. User panel feedback informed refinement and simplification of the SW-Cl cleaning method and instructions. Conclusion: A chlorine-based method for cleaning catheters, which effectively removed bacteria from catheters reused multiple times, has been developed, tested and refined by users, and captured in an instruction booklet and video for inclusion in a clinical trial.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".