Treatment patterns for women with new episodes of uterine myomas in an insured population in the US
Bibliographic record
Abstract
INTRODUCTION: Uterine myomas are the most common benign tumors in reproductive-aged women and a leading reason for gynecologist visits and hysterectomies in the United States. This study examines the treatment patterns of insured women with new episodes of uterine myomas. MATERIALS AND METHODS: We used administrative claims from a proprietary research database to evaluate services (inpatient, outpatient, and prescription claims) incurred from January 1, 2001 to December 31, 2003. We identified women with CPT or ICD-9-CM codes suggestive of myoma and described treatment patterns for all women with a new episode of myoma and those with abnormal bleeding. RESULTS: The primary study group included 35 329 women with new episodes of care and at least three months of data before and after their index date for myoma. Most women (82.9%) had no code for diagnostic testing in the three months before or after the first myoma marker. Of 14 434 women with one year of follow-up, 26.1% had surgery and 24.7% were treated pharmacologically (oral contraceptives, progestins, or gonadotropin-releasing hormone agonists). Over half (55.1%) of women were untreated, including 45% of those with an ICD-9 code that indicated abnormal bleeding. CONCLUSIONS: Women with new myoma episodes rarely had codes for confirmatory diagnostic tests. Many women with myomas go untreated for at least a year. This is true even for those with evidence of abnormal bleeding. Myoma care may be improved through the introduction of new, safe, and effective therapies.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".