Issues to debate on the Women's Health Initiative (WHI) study: Hormone replacement therapy and acute coronary outcomes: methodological issues between randomized and observational studies
Bibliographic record
Abstract
A large number of observational studies, supported by animal and basic research studies, have shown a protective effect of hormone replacement therapy (HRT) on acute coronary outcomes. The recent large randomized Women's Health Initiative (WHI) study reported the opposite result, i.e. a small risk increase of 29% for acute coronary outcomes under estrogen-progestin treatment. Possible methodological reasons for these discrepancies are discussed. Despite randomization, the reported small increase in risk in the WHI study could be spurious because of differential unblinding of HRT users, which could have resulted in higher detection rates of otherwise clinically unrecognized acute myocardial infarction in these women. We show that altering diagnostic patterns because of unblinding could lower the crude rate ratio of 1.28 to 1.02. In the observational studies, the protective effect may have been exaggerated due to a healthy user bias and to the inappropriate choice of the reference group. Using an alternative reference group, the combined rate ratio of 0.67 was increased to 0.82. The diametrical effects of HRT on acute coronary outcomes found between the observational studies and the WHI Study may be a result not only of bias in the observational studies, but also of bias in the WHI Study.
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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 | 0.479 | 0.582 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".