A plain language summary of the likelihood of symptom relief for patients taking fesoterodine for overactive bladder
Bibliographic record
Abstract
WHAT IS THIS SUMMARY ABOUT?: . Overactive bladder is a medical condition that causes an urgent need to urinate, which can cause accidental urination. Fesoterodine is a medication used to treat overactive bladder. Because we don't know how likely it is that an individual patient will achieve a level of improvement in their overactive bladder symptoms, researchers analyzed results of 6 studies of patients with overactive bladder who were treated with fesoterodine. WHAT WERE THE RESULTS?: Although complete resolution of all symptoms was rare with fesoterodine treatment, a resolution of accidental urination was more common, which is the most important treatment goal for many patients. After taking fesoterodine, episodes of accidental urination were more likely to be reduced or completely absent than episodes of an urgent need to urinate. WHAT DO THE RESULTS OF THE STUDY MEAN?: These results can help patients with overactive bladder understand their own chances of treatment success with fesoterodine and can help doctors support their patients on what to expect regarding their specific symptoms and concerns. ▪Toviaz (fesoterodine) is approved to treat the condition that is discussed in this summary. Approval varies from country to country; please check with your local health provider for more details. ▪This summary reports the combined results of 6 studies. The results of individual studies may vary from the combined study results presented here. Individuals should make treatment decisions based on all available evidence. ClinicalTrials.gov NCT number: NCT01302054, NCT01302067, NCT00444925, NCT00611026, NCT00220363, and NCT00138723.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.123 | 0.019 |
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".