Diagnosis and classification of uterine fibroids
Bibliographic record
Abstract
Uterine fibroids, or leiomyomas, are benign uterine tumors with a lifetime prevalence of approximately 75%. While only a minority become symptomatic, their impact on quality of life remains profound owing to heavy menstrual bleeding, bulk symptoms, and reproductive dysfunction. Interestingly, fibroids are not reliably diagnosed on physical examination, nor can their impact be predicted through palpation. Consequently, the diagnosis and phenotypic categorization of uterine fibroids relies primarily on imaging to guide subsequent clinical management. Ultrasound should be the first-line diagnostic modality, and magnetic resonance imaging should be reserved for complex cases and/or surgical planning. Adherence to standardized structured reporting across imaging modalities is critical to improve diagnostic accuracy, differentiate fibroids from conditions such as adenomyosis or malignancies, predict therapeutic responses, and plan surgical interventions. Imaging reports should follow the FIGO classification system for uterine fibroids, to enable a standardized description of their relationship to the endometrium, myometrium, and uterine serosa.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".