Does asymptomatic bacteriuria increase the risk of adverse events or modify the efficacy of intradetrusor onabotulinumtoxinA injections?
Bibliographic record
Abstract
AIM: To assess the impact of asymptomatic bacteriuria (ASB) on the safety and efficacy of intradetrusor onabotulinumtoxinA injections in patients with overactive bladder and neurogenic detrusor overactivity. METHODS: We reviewed the medical records of patients who had received onabotulinumtoxinA between 2009 and 2014. Safety analysis was based on the appearance of urinary tract infections (UTIs), hematuria, and need for hospitalization because of related adverse event(s) in the month after injection. Patients who underwent urodynamic study before and 3 months after the first onabotulinumtoxinA treatment were included in efficacy analysis. Changes in maximal cystometric capacity (MCC), bladder compliance (BC), maximal detrusor pressure at maximal involuntary detrusor contraction (Pdetmax), and detrusor leak point pressure (DLPP) were assessed. RESULTS: Totally, 183 patients underwent 457 injection sessions. ASB was found in 38.8% (185) of urine cultures taken before injections. After treatment, 49 patients (with or without ASB) developed UTI. Urosepsis did not occur. The odds ratio of UTI in patients with ASB was 16.48. The efficacy cohort, consisting of 83 patients, showed that ASB had no significant effect on any of the efficacy parameters (MCC-risk ratio [RR]: 0.93, 95% confidence interval [CI]: 0.72-1.21; BC-RR: 0.88, 95% CI: 0.62-1.24; Pdetmax-RR: 0.9, 95% CI: 0.69-1.21; DLPP-RR: 1.69, 95% CI: 0.72-3.97). CONCLUSIONS: ASB is common among patients who are candidates for intradetrusor onabotulinumtoxinA treatment. ASB increases the risk of UTI, but does not heighten the risk of urosepsis, hospitalization, or therapy failure. This study should lead to the reconsideration of current recommendations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".