The use of urodynamic studies for the followup of neurogenic bladders treated with onabotulinumtoxinA
Bibliographic record
Abstract
INTRODUCTION: Intradetrusor injection of onabotulinumtoxinA (BoNTA) is well-established as treatment for patients with neurogenic bladders. Urodynamics (UDS) is used at regular intervals during followup to monitor intravesical pressure. With regards to the discomfort and risks associated with UDS, our objective was to assess if UDS done at regular intervals in the followup of neurogenic bladders treated with BoNTA had an impact on management. METHODS: We retrospectively analyzed the medical records of adult patients with neurological disorders treated with BoNTA for either detrusor overactivity or low bladder compliance at the Institut de Réadaptation en Déficience Physique de Québec (IRDPQ). At our centre, UDS was routinely performed at baseline, three months after the first treatment, then three months after every fifth set of injections. RESULTS: We identified 57 patients with neurological disorder treated with intravesical BoNTA. Each patient had between one and 19 sets of injections (mean 5.61 injections) and 1-6 followup UDS (mean 2.09). Of the 119 followup UDS reviewed at our centre, three UDS (2.5%) resulted in a modification of the urinary tract management from BoNTA to bladder augmentation. Two regimens were suspended and one was ended due to patient preference. CONCLUSIONS: Our study showed that UDS at pre-set intervals for followup of patients receiving BoNTA injections were rarely associated with modifications in the treatment course. Therefore, UDS should only be performed in cases where there are changes in the patient's symptoms or if the urologist suspects that the treatment response is suboptimal.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".