Determinants of hormone replacement therapy duration among postmenopausal women with intact uteri
Bibliographic record
Abstract
OBJECTIVE: To investigate factors associated with hormone replacement therapy (HRT) duration among postmenopausal women with intact uteri. DESIGN: A Cox proportional hazard model on time to HRT discontinuation is estimated for 2,632 postmenopausal HRT users with intact uteri who began a new episode of treatment between January 1990 and December 1994 in Saskatchewan, Canada. RESULTS: Major contraindicating medical events were highly associated with HRT discontinuation among postmenopausal women. Women who were diagnosed with uterine cancer while taking HRT were almost four times as likely to discontinue HRT, and women who were diagnosed with breast cancer while taking HRT were nearly five times as likely to discontinue HRT. Other statistically significant factors associated with the duration of HRT episodes include administration mode and the ability to try different types and strengths of HRT. Women initiating HRT with a transdermal patch were 50% more likely to discontinue it. Women who were willing and able to experiment with different HRT reduced their likelihood of discontinuing by one-half to three-fourths. CONCLUSIONS: Although some of the factors associated with the hazard of HRT discontinuation among postmenopausal women who are taking the treatment for preventive benefits are immutable, clinicians may influence HRT continuation rates through initial drug choice or modifications in drug type or regimen over the course of therapy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".