Disease Burden Among Canadian Women With Symptomatic Uterine Fibroids: Interim Results of CAPTURE [1H]
Bibliographic record
Abstract
INTRODUCTION: The CAnadian women wiTh Uterine fibroids (UF) REgistry (CAPTURE) was designed to document real-world data about disease burden, management, and outcomes of symptomatic UFs. METHODS: This up to 2-year, prospective, observational, practice-based study will include ∼1000 premenopausal adult women referred for treatment of symptomatic UF at ≥12 clinical practice sites across Canada. RESULTS: Among 461 women enrolled to date, 64.6% were White, with mean ± SD age of 43.3 ± 6.9 years and BMI of 26.4 ± 6.2 kg/m2. Comorbidities included anemia (43.4%), known/suspected endometriosis (7.2%), hypertension (6.3%), and diabetes (2.4%). Most frequently reported symptoms (>50%) included heavy menstrual bleeding (82.0%), pelvic discomfort (80.0%), dysmenorrhea (77.8%), urinary frequency (73.6%), pelvic pain (68.5%), low back pain (62.5%), bulk symptoms (60.2%), and urinary urgency (59.6%). Time since first symptoms was < 1 year for 14.2%, 1 to < 3 years for 25.1%, 3 to < 5 years for 16.4%, and ≥5 years for 44.3%. MRI or ultrasound indicated 36.2% had 1 UF, 34.1% had 2 to 4, and 29.6% had ≥5. At baseline, 88% reported bleeding in the past 3 months with mean ± SD Aberdeen menorrhagia severity scale score of 34.9 ± 20. As measured by the UF symptom and quality of life questionnaire (UFS-QoL), mean ± SD symptom severity was 48.7 ± 22.7 and QoL was 52.3 ± 25.1. CONCLUSION: CAPTURE is the first registry worldwide to examine real-world practice-based management of women with UF. The 461 patients enrolled to date, of the targeted 1000, demonstrate a high burden of disease and a poor QoL. Impact of interventions on QoL will be evaluated at follow-up visits.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".