SCORE2 charts assign a different risk category than the underlying model in a large portion of Polish primary care patients
Bibliographic record
Abstract
Abstract Aims This study aims to compare the estimates provided by the SCORE2/SCORE2-OP charts with the estimates obtained by using SCORE2 and SCORE2-OP risk model equations. Methods and results Patients were recruited from 16 major Polish administrative regions proportionally to the number of inhabitants through primary care practices. Patients with risk parameters exceeding permitted values and with comorbidities putting them in the very high-risk category were excluded, and a total of 6621 patients were studied in this analysis. Each eligible patient was assigned SCORE2/SCORE2-OP risk estimate and category using the SCORE2 chart and the equations for high-risk countries provided in the original SCORE2/SCORE2-OP papers. Compared with the underlying model estimate, SCORE2 chart estimate was ≥20% higher for a total of 2164 (32.7%) patients and 20% lower for a total of 366 (5.5%) patients; the assigned risks differed significantly between (P < 0.0001; Wilcoxon rank test). It was also found that SCORE2 charts assigned 1278 (19.3%) patients to a higher risk category and 105 (1.6%) patients to a lower category (P < 0.001; McNemar–Bowker test). A clear saw-tooth pattern was observed in the difference between equation and chart estimate at different values of systolic blood pressure, age, and non-HDL, and a clear bias towards risk overestimation at higher HDL concentrations was also noted for the charts. Conclusion SCORE2/SCORE2-OP charts tend to overestimate cardiovascular risk relative to the core risk models. The use of calculators that directly apply the model might help to determine more accurately the indications for blood pressure- and lipid-lowering therapies. Lay summary In this paper, estimates obtained from SCORE2 and SCORE2-OP cardiovascular risk charts were compared with estimates from the underlying risk models.SCORE2 and SCORE2-OP charts have a clear tendency to overestimate cardiovascular risk relative to the model obtained in their original publications.The overestimation is more likely at higher HDL values and at the bottom of the non-HDL cholesterol, systolic blood pressure, and age intervals presented in the charts.
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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) | 0.001 | 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 | 0.001 |
| 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".