Efficacy of antibiotic prophylaxis among intermittent catheter users with different neurologic diseases: A secondary analysis of the AnTIC Trial
Bibliographic record
Abstract
To use existing clinical trial data to assess the impact of prophylactic antibiotics on the 1-year UTI rate among people with different neurologic diseases, and to determine if UTIs impact renal function. We conducted a secondary analysis of community dwelling participants with a neurologic disease and intermittent catheter use who participated in a 12-month randomized trial (AnTIC) of low dose antibiotic prophylaxis. We calculated incident rate ratios (IRR) of symptomatic UTIs that required antibiotics. Renal function was assessed using the estimated glomerular filtration rate. We identified 138 patients who had a neurologic disease (multiple sclerosis (25%), spinal cord injury (21%), spina bifida (18%), and other disorders (36%)). The incidence of symptomatic, antibiotic treated urinary infections was 1.48 per person–year in the prophylaxis group, and 2.51 per person–year in the usual care group; the IRR was 0.59 (95% CI 0.46, 0.76) in favor of continuous antibiotic prophylaxis. The IRR was lowest (most protective) among those with spinal cord injury (IRR 0.23, p < 0.01) and highest (least protective) in those with spina bifida (IRR 0.85, p = 0.57). There were small, non-significant decreases in renal function that did not differ by randomization. There were no significant differences in pre- and post-study renal function based on the number of UTIs participants experienced. Continuous antibiotic prophylaxis may be more effective for certain patient populations with neurologic lower urinary tract dysfunction. Renal function is not significantly impacted by a higher number of UTIs over the course of one year.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".