Evaluating the 8‐item overactive bladder questionnaire (OAB‐v8) using item response theory
Bibliographic record
Abstract
AIMS: The OAB-v8 is a patient-reported outcome questionnaire used to screen for overactive bladder and measure symptom bother. This study uses modern validation methods to assess the item and test characteristics of the OAB-v8, and determine whether it should be scored differently for men and women. METHODS: A secondary analysis of data from patients with lower urinary tract symptoms prospectively recruited from a urology clinic in Calgary, Canada. Item-response theory (IRT) was used to evaluate the dimensionality, reliability, and validity of the OAB-v8, and differential functioning analysis (DIF) was used to determine whether the OAB-v8 should be scored differently for men and women. RESULTS: A total of 1128 patients were included for analysis, of which 28% were female. Mean OAB-v8 scores indicated meaningful levels of symptom bother, and women scored 2 points higher, on average, than men (P = 0.005). Reliability was high (Cronbach's alpha = 0.901) for a wide range of patients (between -2 and 2.5 standard deviations of the mean). Samejima's graded response model best fit the data, and the scale demonstrated essential unidimensionality. Items #5 and #6 were excessively correlated. There was no evidence of differential functioning. CONCLUSIONS: OAB-v8 demonstrated high reliability and would be appropriate for a wide range of patients. Items #5 and #6 may need to be re-worded. The OAB-v8 should not be scored differently for men and women, although women do appear to experience higher average levels of symptom bother from OAB than men.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".