External validation of the systematic coronary risk evaluation 2 (SCORE2) and SCORE2-older persons in cancer patients
Bibliographic record
Abstract
AIMS: The 2022 European Society of Cardiology cardio-oncology guidelines recommend cardiovascular disease (CVD) risk stratification for cancer patients and suggest using SCORE2 and SCORE2-OP. However, these models have not been validated or specifically adapted for cancer populations. Our aim was to refine the SCORE2 and SCORE2-OP models to accurately predict 10-year fatal and non-fatal CVD risk in cancer patients. METHODS AND RESULTS: We included 1622 patients from the HUNT3 study (2006-08) who were diagnosed with cancer within 4 years after their enrolment and followed until 2023 linked to national registries. The primary outcome was a composite of myocardial infarction (MI), stroke, or CVD mortality. Model performance was assessed using Harrel's C-statistic and calibration curves. Both models were recalibrated by applying a multiplicative adjustment factor based on expected-observed (E/O) ratios. The most prevalent cancers were gastrointestinal (23%), prostate (17%), and breast (14%). Mean age was 65.2 years, 52% were female. During a median follow-up of 8.8 years [inter-quartile range 1.9-12.6], 252 CVD events (39% MI, 36% stroke, 25% CVD deaths) and 708 non-CVD deaths occurred. SCORE2 initially underestimated CVD risk (E/O ratio for men and women: 0.91 and 0.63, respectively) but showed adequate agreement after recalibration. C-statistics for SCORE2 was 0.693 [95% confidence interval (CI) 0.643-0.743], and 0.730 (95% CI 0.676-0.784) after excluding those not surviving the first 2 years. For SCORE2-OP, the C-statistics were 0.586 (95% CI 0.529-0.643) and 0.648 (95% CI 0.577-0.720). CONCLUSION: SCORE2 underestimated CVD risk in cancer patients. After recalibration, the model may serve as a valuable tool for risk stratification in cancer patients.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".