© 2002 Canadian Medical Association or its licensors Research
Bibliographic record
Abstract
Research letter During the past 2 decades the health risks and bene-fits of estrogen replacement therapy (ERT) havebeen the focus of intensive research and scientific debate. Recommendations for its use by asymptomatic post-menopausal women are nevertheless still limited by many questions that remain unanswered.1 Despite the uncertainty surrounding the overall impact of ERT on health, US data indicate that the prevalence of hormone use has been steadily increasing since the 1980s.2 Longitudinal popula-tion-based data describing ERT use by postmenopausal women in Canada are lacking. We therefore examined the trends in the prevalence of estrogen use by peri- and post-menopausal women in Saskatchewan from 1981 to 1997. We used Saskatchewan Health’s computerized prescrip-tion drug plan database as the source of drug-dispensing information.3 Women living in the province between 1981 and 1997 were selected from Saskatchewan Health records to participate in 2 population-based case–control studies,4,5 and the control subjects formed a cohort of peri- and post-menopausal women for this analysis. At the time of sampling, the women were 45 years of age or older, did not have a diagnosis of cancer (except for non-melanoma skin cancer and can-cer of the cervix in situ), were regis-tered with Saskatchewan Health for at least 5 years and were eligible for out-patient prescription drug plan benefits. The study was approved by the Research Ethics Committee of the
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.678 | 0.518 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".