Role of videourodynamics, imaging, and cystoscopy in patients with recurrent urinary tract infections: Should we throw in the kitchen sink?
Bibliographic record
Abstract
Purpose:: Recurrent urinary tract infection (rUTI) remains a common outpatient problem with discordance and paucity of evidence for management. This study aims to evaluate the role of videourodynamics (VUD), additional imaging, and cystoscopy in the complete workup of these patients. Materials and Methods:: A retrospective review was performed on 1421 consecutive patients referred for physician performed VUD. After exclusion criteria, 170 patients were included. Ethics approval was obtained, followed by data collection, and analysis of demographics, symptoms, cystoscopy results, imaging, and VUD parameters. Statistical analyses were performed with IBM SPSS Statistics Version 28. Statistical significance was defined by an alpha level of P ≤0.05. Results:: Overall, 117/170 (69%) had identifiable causes of rUTI identified on VUD. There was a statistically significant difference (p<0.001) in identifying a cause in those with voiding symptoms (93/114 = 82%) compared to those without (24/56 = 43%). Gender was not predictive of an identifiable cause on VUD (p=0.47). Neither was a neurogenic history (p=0.11), diabetes (p=0.97), or age (p=0.89). Additional imaging was not diagnostic for rUTI cause. No malignancy was identified on imaging or cystoscopy. Conclusion:: In patients with rUTI, VUD may be an important investigative step to find a possible underlying cause, but it is a scarce resource. As VUD has a higher detection rate in patients with voiding symptoms, by first screening for these patients on history, VUD can be used judiciously. Cystoscopy and additional imaging were not as helpful in identifying a target treatment plan for rUTI, when a VUD had already been done.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".