The FIBROID Registry
Bibliographic record
Abstract
OBJECTIVES: To investigate the change in symptom severity and health-related quality of life among patients treated with uterine artery embolization for leiomyomata. METHODS: Using the Fibroid Registry for Outcomes Data (FIBROID), a multicenter, prospective, voluntary registry of patients undergoing uterine embolization for leiomyomata, we studied changes in symptom status, health-related quality of life, subsequent care, menstrual status, and satisfaction with outcome. Health-related quality-of-life and symptom status were measured using the Uterine Fibroid Symptom and Quality of Life, a leiomyoma-specific questionnaire. Summary statistics were used to describe the data set and multivariate analyses to determine predictors of outcome at 12 months. RESULTS: Of 2,112 eligible patients, follow-up data were obtained on 1,797 (85.1%) at 6 months and 1,701 (80.5%) at 12 months. At 12 months, the mean symptom score had improved from 58.61 to 19.23 (P < .001), whereas 5.47% of patients had no improvement. The mean health-related quality-of-life score improved from 46.95 to 86.68 (P < .001), whereas 5.0% did not improve. In the first year after embolization, hysterectomy was performed in 2.9% of patients, with 3.6% requiring gynecologic interventions by 6 months and an additional 5.9% between 6 and 12 months. Amenorrhea as a result of embolization occurred in 7.3% of patients. Of these, 86% were age 45 or older. Most patients were satisfied with their outcome (82% strongly agree or agree). Predictors of a greater symptom change score include smaller leiomyoma size, submucosal location, and presenting symptom of heavy menstrual bleeding. CONCLUSION: Uterine embolization results in substantial symptom improvement for most patients, with hysterectomy required in only 2.9% of patients in the first 12 months after therapy.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.018 |
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".