Hormone Replacement Therapy Use After Premature Surgical Menopause and Impact on Subsequent Health Services
Bibliographic record
Abstract
Introduction/Background: It is well-recognized that premature surgical menopause without subsequent hormone replacement therapy (HRT) is associated with increased morbidity and mortality. Our objective was to describe HRT use in a cohort of women from British Columbia (BC), Canada with premature surgical menopause.Methodology: A population-based retrospective cohort study of women who underwent surgical menopause between ages 19-50, identified from Medical Service Plan Payment Information from BC, and linked to prescription histories from the provincial PharmaNet database from 2004-2014. Women with personal history of cancer were excluded. Results: A total of 11,864 women were included. Median age of surgical menopause was 43 years. Median follow-up duration was 5.5 years (range: 0-10 years). The majority of women did not have a BRCA mutation (99.1%). They underwent bilateral salpingo-oophorectomy (90%) or bilateral oophorectomy (10.4%), and 50.5% had concurrent hysterectomy. There were 196 (1.7%) diagnosed with malignancy. The remainder had benign diagnoses, the most common of which were adnexal masses (17%), endometriosis (17.9%), and abnormal bleeding (14.3%). During the 10-year study period, only 55.4% had ever used HRT. Use of HRT for <6 months, 6-12 months, 1-5 years, and >5 years after surgery were 38.2%, 8.9%, 32.7%, and 20.2%, respectively. Those who did not use HRT had more physician visits for cardiovascular disease (19% vs. 9%) and osteoporosis (13% vs. 9%) compared to ever-users, respectively (p = 0.002). Conclusion: In this large population-based study investigating HRT rates following premature surgical menopause, only half of these women ever used HRT, and a greater proportion of those who did not use HRT had physician visits for cardiovascular diseases and osteoporosis. These results underscore a need for education and ongoing follow-up in this patient population.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".