Estrogen replacement therapy: determinants of persistence with treatment
Bibliographic record
Abstract
OBJECTIVE: To evaluate the persistence rate for estrogen therapy and to identify its determinants. METHODS: From the Quebec health insurance database we chose a cohort of 4527 women 35 years and older who received social assistance and were new users of estrogen between January 1989 and December 1997. Incident use was defined by the absence of any dispensed prescription of estrogen in the 3 years before the index date (date of first dispensed prescription). We estimated the cumulative persistence rate of treatment by Kaplan-Meier failure time analysis and identified its determinants with the Cox proportional hazards model. RESULTS: From the initial cohort, 3395 women (75%) renewed their first dispensed prescription and 905 (20%) continued treatment after 4 years. The determinants measured at the index date and significantly associated with a better persistence rate (relative risk [RR]) were younger than 60 years (RR 1.15, 95% confidence interval [CI] 1.01, 1.30), low dosage (RR 1.49, 95% CI 1.32, 1.70), continuous progestin combination (RR 1.40, 95% CI 1.27, 1.54), and a gynecologist as the first prescribing physician (RR 1.15, 95% CI 1.03, 1.21). Also, coronary heart disease or at least one risk factor for it in the year before the index date was associated with a better persistence rate for estrogen replacement therapy (RR 1.15, 95% CI 1.05, 1.22). CONCLUSIONS: The persistence rate for estrogen therapy is poor, implying that few women take it long enough to benefit from it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".