Urinary catheter balloons should only be filled with water: testing the myth
Bibliographic record
Abstract
OBJECTIVE: To test the hypothesis that urinary catheter balloons filled with sterile water, saline or glycine have equivalent rates of failure to deflate. MATERIALS AND METHODS: This was an in vitro equivalence study designed to test whether saline or glycine are neither substantially worse nor substantially better than water in terms of balloon-deflation failure rates. Glycine was chosen as the third arm, as it is readily available during endoscopic procedures and would be useful to use in such situations. We hypothesised that balloon-deflation failure rates using saline or glycine were no worse than water by 10%. We calculated the sample size for equivalence testing; 600 catheters were randomized by computer-generated random numbers to receive 10 mL of water, saline or glycine, and then immersed in a heated artificial urine solution for 6 weeks. The catheter balloons were then deflated, noting any failures to deflate and recording the deflation volumes. RESULTS: There was no failure to deflate in all 600 catheters. The median deflation volume for water, saline and glycine was 9.0, 9.2 and 9.1 mL, respectively (P < 0.001 Kruskal-Wallis test). Post-hoc pair-wise comparisons showed that the deflation volume difference between water and saline was significant (P < 0.001), as was that between water and glycine (P < 0.001). The practical implication of this difference is not apparent from this study. CONCLUSIONS: The use of saline or glycine in catheter balloons has an equivalent deflation failure rate to using water, which in this study was zero.
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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".