Contemporary management of uterine fibroids: focus on emerging medical treatments
Bibliographic record
Abstract
OBJECTIVE: This review provides an overview of therapeutic options, with a specific focus on the emerging role of medical options for UF management. RESEARCH DESIGN AND METHODS: PubMed, Google Scholar, and Cochrane Systematic Reviews were searched for articles published between 1980 and 2013. Relevant articles were identified using the following terms: 'uterine fibroids', 'leiomyoma', 'heavy menstrual bleeding', and 'menorrhagia'. The reference lists of articles identified were also searched for other relevant publications. RESULTS: Because of the largely benign nature of UFs, the most conservative options that minimize morbidity/risk and optimize outcomes should be considered. Watchful waiting, or no immediate intervention combined with regular follow-up, is an appropriate option for the majority of UF patients who experience no symptoms. For women with symptomatic UFs, the optimal treatment should restore quality of life through rapid relief of UF signs and symptoms, reduce tumor size for a sustained period, and maintain or improve fertility. Invasive surgical treatments, such as hysterectomy, have historically been the mainstay of UF treatment. Less invasive surgical and interventional techniques, such as myomectomy, uterine artery embolization, endometrial ablation, and myolysis provide alternatives to hysterectomy. Until recently, medical management of UFs was characterized by short-term treatments and therapies that provided symptomatic control. In addition to controlling abnormal uterine bleeding, newer medical therapies, including the recently Health-Canada-approved ulipristal acetate, act directly to shrink the tumor. Although no agent is currently approved for such use, emerging evidence suggests the potential for long-term medical management of UFs. CONCLUSIONS: The advent of novel medical therapies may diminish the long-held reliance on more invasive surgical UF treatment options.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".