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 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 | 0.059 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".