A Call-to-Action for Clinicians to Implement Evidence-Based Best Practices When Caring for Women with Uterine Fibroids
Bibliographic record
Abstract
Uterine fibroids are common benign tumors that occur in up to 80% of women. Approximately half of the women affected experience considerable physical, psychological, and economic burdens and impact on quality of life due to symptoms such as heavy menstrual bleeding, pelvic pain, and infertility. Several medical and surgical options are available to treat uterine fibroids; however, healthcare providers may benefit from practical guidance in the development of individualized treatment plans based on a personalized approach. Medical treatments and minimally invasive procedures are generally preferred by most patients before considering more invasive, higher risk surgical interventions. In general, patient-centered, uterine-preserving procedures may be prioritized based on the patient's goals and the clinical scenario. Occasionally, hysterectomy may be the preferred treatment option for some patients who require definitive treatment. This call-to-action highlights recent challenges to patient care, including radical shifts in physician-patient interactions due to the COVID-19 pandemic and recent changes to evidence-based, clinically approved therapies. This report also reviews contemporary recommendations for women's health providers in the diagnosis and medical and surgical management of uterine fibroids. This call-to-action aims to empower healthcare providers to optimize the quality of care for women with uterine fibroids utilizing the best available evidence and best practices.
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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 | 0.115 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.009 | 0.027 |
| Research integrity | 0.054 | 0.063 |
| Insufficient payload (model declined to judge) | 0.026 | 0.016 |
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".