SCORE2 Asia-Pacific: a comprehensive approach to prevention of cardiovascular disease
Bibliographic record
Abstract
This commentary refers to ‘Risk prediction of cardiovascular disease in the Asia-Pacific region: the SCORE2 Asia-Pacific model’, by S.H.J. Hageman et al., https://doi.org/10.1093/eurheartj/ehae609 and the discussion piece ‘Risk prediction for cardiovascular diseases in Asia-Pacific: to separate subtypes or not, that is a new question’, by S. Yang and J. Lv, https://doi.org/10.1093/eurheartj/ehae854. In the commentary from Yang and Lv,1 the differences in stroke subtype incidence in Asia are discussed, as well as their consequences for risk prediction and subsequent treatment. We appreciate their engagement with our work and their valuable insights, particularly regarding the model’s performance and the considerations relevant to the Asia-Pacific region’s distinct cardiovascular disease (CVD) characteristics. The authors suggest to provide additional calibration metrics to assess the SCORE2 Asia-Pacific model’s performance. While we completely agree with the authors that the calibration is the most relevant measure for the clinical practice, it is our view that more objective tests, such as the Nam–D’Agostino test, may not provide additional value given our extensive data sources.2 With large datasets, these tests will nearly always indicate statistically significant differences, even when those differences lack practical or clinical significance. A plot in deciles is an alternative we had considered during the development of our model. However, given the strong relation between age and CVD incidence, these plots generally show a broadly similar result (see example Figure 1). We had chosen the age group-based plot as this best reflects our recalibration method, which was also based on 5-year age groups. This way, readers cannot only judge whether predicted risks match the observed incidence but also whether the recalibration efforts have succeeded over the whole age range in the respective risk region.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".