Absolute CVD risk, stratified by risk score, was 20% higher in primary care patients with CVD than in those without CVDCommentary
Bibliographic record
Abstract
How do risks of cardiovascular disease (CVD) events compare in primary care patients with and without a history of CVD after adjusting for traditional CVD risk factors? ### Design: prospective cohort study with a mean 2 years of follow-up. ### Setting: primary care practices in Auckland, New Zealand. ### Patients: 35 760 patients 30–74 years of age (mean age 54 y, 57% men, 10% with a history of CVD) who had a CVD risk score calculated using the web-based PREDICT clinical decision support program. ### Description of prediction guide: based on the Framingham risk score, PREDICT uses traditional CVD risk factors (age, sex, diabetes, smoking, blood pressure, and cholesterol concentrations) to classify patients as having <5%, 5 to <10%, 10 to <15%, 15 to <20%, or ⩾20% 5-year risk of a CVD event. ### Outcome: first CVD event (acute coronary syndrome, ischaemic or haemorrhagic stroke, …
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".