Predictors of variability in urinary incontinence and overactive bladder symptoms
Bibliographic record
Abstract
AIMS: We used data from the General Longitudinal Overactive Bladder Evaluation (GLOBE) to understand predictors of variation in urgency and urinary incontinence (UI) symptoms over time. METHODS: A random sample of Geisinger Clinic primary care patients (men and women) 40+ years of age were recruited for a survey of bladder control symptoms at baseline and 12 months later. Symptom questions used a 4-week recall period. Composite scores were derived for urgency and UI frequency. Logistic regression was used to evaluate predictors of variation in scores at cross-section and longitudinally. RESULTS: A majority of those with UI symptoms and almost 40% of those with urgency symptoms reported episodes of once a week or less often; 17% had symptoms a few times a week or more often. Twenty-one percent with urgency symptoms and 25% with UI symptoms at baseline did not have active symptoms 12 months later. The strongest predictors of active symptoms at follow-up were baseline symptom score and duration of time since first onset of symptoms. Of those with no urgency symptoms at baseline, 22% had urgency at 12 months. Among those with no UI symptoms at baseline, 13% had UI symptoms 12 months later. Among the latter, age (males only) and BMI were the strongest predictors of symptoms at follow-up. CONCLUSIONS: Inter-individual and intra-individual occurrences of urgency and UI symptoms are highly variable in the general population. Use of established predictors to select individuals with less variability in symptoms may help to reduce placebo rates in clinical trials.
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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 | 0.002 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".