Development of a core set of outcome measures for OAB treatment
Bibliographic record
Abstract
INTRODUCTION AND HYPOTHESIS: Standardized measures enable the comparison of outcomes across providers and treatments giving valuable information for improving care quality and efficacy. The aim of this project was to define a minimum standard set of outcome measures and case-mix factors for evaluating the care of patients with overactive bladder (OAB). METHODS: The International Consortium for Health Outcomes Measurement (ICHOM) convened an international working group (WG) of leading clinicians and patients to engage in a structured method for developing a core outcome set. Consensus was determined by a modified Delphi process, and discussions were supported by both literature review and patient input. RESULTS: The standard set measures outcomes of care for adults seeking treatment for OAB, excluding residents of long-term care facilities. The WG focused on treatment outcomes identified as most important key outcome domains to patients: symptom burden and bother, physical functioning, emotional health, impact of symptoms and treatment on quality of life, and success of treatment. Demographic information and case-mix factors that may affect these outcomes were also included. CONCLUSIONS: The standardized outcome set for evaluating clinical care is appropriate for use by all health providers caring for patients with OAB, regardless of specialty or geographic location, and provides key data for quality improvement activities and research.
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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 | 0.107 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".