Differences in the incidence of urinary tract infections between neurogenic and non‐neurogenic bladder dysfunction individuals performing intermittent catheterization
Bibliographic record
Abstract
PURPOSE: To measure the incidence and severity of urinary tract infections (UTI) in intermittent catheter (IC) users with neurogenic and non-neurogenic diagnoses. MATERIALS AND METHODS: Administrative health insurance claims data from the IBM MarketScan® Database between January 1, 2015 and December 31, 2019, were analyzed. New IC-users with neurogenic lower urinary tract dysfunction (NLUTD); IC-users without NLUTD (non-NLUTD); and age-and-sex-matched general population without IC use (GEN) were compared. Individuals were followed for one year after initial IC utilization or random index date for GEN. The primary outcome was a patient seeing a physician or attending a hospital for a UTI (measured with a primary or secondary diagnosis code related to a UTI). UTI incidence, hospitalizations, and length of hospital stay were compared. RESULT: We identified 6944 NLUTD, 5102 non-NLUTD, and 120 426 GEN individuals. The annualized UTI incidence was higher in IC-users (54.9% NLUTD IC-users and 38.9% non-NLUTD IC-users) compared to GEN individuals (9.8%) (p < 0.001 between groups). Hospitalization for UTI was more common in NLUTD and non-LUTD (11.3% and 4.0%, respectively) compared with GEN individuals (1.0%) (p < 0.001 between groups). NLUTD individuals had a greater average length of hospital stay than non-NLUTD (2.2 ± 3.6 vs. 1.6 ± 2.1 days, p < 0.001). CONCLUSION: IC users had a significantly higher incidence of UTIs than the general population. NLUTD IC-users had a higher incidence of UTIs that required hospitalization compared to non-NLUTD individuals. Strategies to decrease the patient and healthcare burden of UTIs in those that catheterize should be prioritized.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".