Development of an overactive bladder assessment tool (BAT): A potential improvement to the standard bladder diary
Bibliographic record
Abstract
AIMS: To develop a comprehensive patient-reported bladder assessment tool (BAT) for assessing overactive bladder (OAB) symptoms, bother, impacts, and satisfaction with treatment. METHODS: Subjects were consented and eligibility was confirmed by a recruiting physician; subjects were then scheduled for in-person interviews. For concept elicitation and cognitive interviews, 30 and 20 subjects, respectively, were targeted for recruitment from US sites. All interviews were conducted face-to-face, audio-recorded, transcribed verbatim, anonymized, and analyzed using a qualitative data analysis software program. A draft BAT was created based on the results of the concept elicitation interviews and further revised based on cognitive interviews as well as feedback from an advisory board of clinical and patient-reported outcome (PRO) experts. RESULTS: Nocturia, daytime frequency, and urgency were reported by all subjects (n = 30, 100.0%), and incontinence was reported by most subjects (n = 25, 83.3%). The most frequently reported impacts were waking up to urinate (n = 30, 100.0%), embarrassment/shame (n = 24, 80.0%), stress/anxiety (n = 23, 76.7%), and lack of control (n = 23, 76.7%). Following analysis, item generation, cognitive interviews, and advisory board feedback, the resulting BAT contains four hypothesized domains (symptom frequency, symptom bother, impacts, and satisfaction with treatment) and 17 items with a 7-day recall period. CONCLUSIONS: The BAT has been developed in multiple stages with input from both OAB patients and clinical experts following the recommended processes included in the FDA PRO Guidance for Industry. Once fully validated, we believe it will offer a superior alternative to use of the bladder diary and other PROs for monitoring OAB patients in clinical trials and clinical practice.
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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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".