The epidemiology and pathogenesis of uterine fibroids
Bibliographic record
Abstract
Uterine fibroids, or leiomyomas, are the most common benign tumors of the female reproductive tract, ultimately affecting a majority of women worldwide, primarily during their reproductive years. While their origin appears genetic, manifesting in monoclonal tumors, diverse features and mechanisms contribute to their growth and further development. Understanding the interplay between epidemiological and biological factors is vital for clinicians and essential for investigators, whether basic, translational, clinical, or epidemiological, who aim to shed light on this ubiquitous clinical problem. Leiomyomas are experienced globally, and while there are relatively minor differences in the lifetime prevalence for women, the tumors appear to develop in those of African ancestry earlier than white women. While most leiomyomas are not symptomatic, those that cause symptoms can adversely affect lifestyle, physical function, and fertility and exert substantial socioeconomic pressure on healthcare systems. Because most leiomyomas are asymptomatic, it is also apparent that, in many cases, presenting symptoms may not be caused by the tumors themselves. As a result, it is essential that the clinician understand the pathogenesis of these tumors and how they manifest with symptoms. Despite their prevalence and evolving understanding of genetic, racial, and environmental factors contributing to their growth and development, much remains to be learned about these ubiquitous tumors in a way that can inform strategies for prevention, early detection, and effective therapy. This paper reviews the current understanding of the epidemiology and pathogenesis of uterine fibroids, highlighting key risk factors, genetic and molecular mechanisms, and implications for public health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".