Cardiotoxicity of Use of Sequential Aromatase Inhibitors in Women With Breast Cancer
Bibliographic record
Abstract
The association between use of aromatase inhibitors (AIs) and cardiovascular outcomes is controversial. While some observational studies have assessed the cardiovascular safety of AIs as upfront treatments, their cardiotoxicity as sequential treatments with tamoxifen remains unknown. Thus, we conducted a population-based cohort study using data from the United Kingdom Clinical Practice Research Datalink linked to the Hospital Episode Statistics and Office for National Statistics databases. We employed a prevalent new-user design to propensity-score match, in a 1:2 ratio, patients switching from tamoxifen to AIs with patients continuing tamoxifen between 1998 and 2016. Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals for the study outcomes (myocardial infarction, ischemic stroke, heart failure, and cardiovascular mortality). Overall, 1,962 patients switching to AIs were matched to 3,874 patients continuing tamoxifen. Compared with tamoxifen, AIs were associated with an increased risk of myocardial infarction (hazard ratio (HR) = 2.08, 95% confidence interval (CI): 1.02, 4.27). The hazard ratios were elevated for ischemic stroke (HR = 1.58, 95% CI: 0.85, 2.93) and heart failure (HR = 1.69, 95% CI: 0.79, 3.62) but not cardiovascular mortality (HR = 0.87, 95% CI: 0.49, 1.54), with confidence intervals including the null value. The elevated hazard ratios observed for the cardiovascular outcomes should be corroborated in future large observational studies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".