Declining incidence of breast cancer after decreased use of hormone-replacement therapy: magnitude and time lags in different countries
Bibliographic record
Abstract
Throughout the latter half of the 20th century, hormone-replacement therapy (HRT) use steadily increased in the Western world. In 2002, the early termination of the Women's Health Initiative trial due to an excess of adverse events attributable to HRT, led to a precipitous decline in its use. Breast cancer incidence began to decline soon thereafter in the USA and several other countries. However, the magnitude of the decline in breast cancer incidence, and its timing with respect to HRT cessation, shows considerable variability between nations. The impact of HRT cessation appears most significant and immediate in countries with the largest absolute decline in HRT use. In countries in which peak prevalence of HRT use was high, several studies have convincingly excluded decreasing rates of mammographic screening as an explanation for the decline in breast cancer incidence. Conversely, in some countries, no decline in breast cancer incidence is apparent that can be readily attributed to declining trends in HRT use. In such cases, declines in breast cancer incidence may be related instead to saturation or decreased utilisation of mammographic screening programmes. In other cases, it is difficult to disentangle the respective influence of trends in HRT use, and the influence of changes relating to mammographic screening. However, irrespective of time lags and varying magnitudes of effect, the data convincingly support a direct association between decreasing HRT use and declining breast cancer incidence.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 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) | 0.000 | 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".