Cardiovascular disease following breast cancer treatment: can we predict who will be affected?
Bibliographic record
Abstract
This editorial refers to ‘Development and validation of a multivariable prediction model for major adverse cardiovascular events after early stage breast cancer: a population-based cohort study’†, by H. Abdel-Qadir et al., on page 3913. There has been a remarkable evolution in the outcomes for women presenting with early breast cancer over the last 40 years, and 10-year survival has doubled from 40% in the 1970s to almost 80% since 2010. This increasing survival has led to the emergence of cardiovascular disease (CVD) as a major cause of morbidity and mortality in breast cancer survivors,1 with one large epidemiological study reporting that CVD overtook breast cancer as the leading cause of death in breast cancer patients at 9 years from cancer diagnosis.2 There are a range of factors influencing the rising incidence of CVD in survivors of early breast cancer including competing risk of ageing, shared risk factors for both CVD and breast cancer (e.g. smoking and obesity), and the impact of the breast cancer treatments.3 CVD can be caused or accelerated by a variety of breast cancer treatments such as heart failure secondary to anthracycline chemotherapy4 and trastuzumab, and coronary artery disease and stroke caused by chest radiation therapy5 and long-term oestrogen suppression.6
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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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".