Promoting patient followup treatment with intra-detrusor onabotulinumtoxinA
Bibliographic record
Abstract
INTRODUCTION: We aimed to characterize patient-related factors that promote followup of repeat onabotulinumtoxinA treatments for overactive bladder via a mixed-methods approach. METHODS: A retrospective chart review was conducted for patients who received intra-detrusor injection of onabotulinumtoxinA at our institution from 2011-2018, who were then surveyed to evaluate their experience, knowledge, and perceptions regarding onabotulinumtoxinA treatment and followup. Patients who received one onabotulinumtoxinA treatment and patients who underwent multiple treatments were compared to assess followup rates following initial treatment, group characteristics, patient comfort, and patient knowledge of needed retreatment. RESULTS: A total of 29.3% of patients received a single treatment and 70.7% of patients received multiple treatments. There was no difference in clinical, demographic, or intake variables between groups. Patients receiving multiple treatments reported having their first procedure in the operating room and reported greater improvement in symptoms and procedure comfort. This group was also more likely to understand that repeat treatments are necessary than those undergoing one treatment. CONCLUSIONS: No research to date has systematically explored patient-reported factors that promote retreatment of onabotulinumtoxinA for overactive bladder. This novel, mixed-methods approach indicates that patient comfort and patient knowledge were the strongest predictors of previous retreatment and anticipated retreatment, suggesting concrete avenues for improved periprocedural patient counselling and education.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".