Intravesical treatment with highly-concentrated hyaluronic acid and chondroitin sulphate in patients with recurrent urinary tract infections: Results from a multicentre survey
Bibliographic record
Abstract
INTRODUCTION: We assess the effectiveness of intravesical instillation of hyaluronic acid (HA) and chondroitin sulphate (CS) as a non-antibiotic treatment option for prophylaxis of recurrent urinary tract infections (UTIs) in female patients. METHODS: This was a retrospective cohort study involving 7 European institutions. We included patients with recurrent UTIs who received intravesical instillations of Ialuril (IBSA International) (50 mL HA 1.6% and CS 2% solution) between January 2010 and March 2012. Medication schedule, length of follow-up, recurrence infection time, number of UTIs/patients/year, patient quality of life, subjective symptoms score, and treatment-emergent side effects were recorded and analyzed. RESULTS: In total, 157 women (mean age: 54.2 ± 4.1 years) were included in the analysis. All patients had at least 12 months follow-up. After 4 weekly and 5 monthly HA-CS bladder instillations, UTI episodes decreased from 4.13 ± 1.14 to 0.44 ± 0.50 (p = 0.01) at 12 months, while recurrent UTI time prolonged from 94.8 ± 25.1 days to 178.4 ± 37.3 days (p = 0.01) at 12 months. An improvement in symptoms and quality of life was achieved. A medium-depth pain after medication instillation was the most reported side effect. Regression model analysis showed significant risk factors in developing new UTI episodes: being more than 50 years old and having more than 4 UTI episodes per year (OR 3.41; CI 95%; 1.51-7.71, p = 0.003 and OR 3.31; CI 95% 1.51-7.22; p = 0.003, respectively). Retrospective design and lack of a control group represent two main limitations of the study. CONCLUSIONS: Restoring glycosaminoglycans bladder layer therapy is a promising non-antibiotic therapy to prevent recurrent UTIs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".