Bibliographic record
Abstract
The Framingham Risk Score (FRS) is the gold standard for assessing cardiovascular (CV) risk.1 Individuals are categorized as having 10-year risk for CV disease event (myocardial infarction, stroke, peripheral arterial disease, congestive heart failure) that is low (0%–10%), moderate (11%–19%) or high (≥20%) based on risk factors that include age, sex, total cholesterol, high-density lipoprotein cholesterol, smoking status and systolic blood pressure. Those with diagnosed coronary artery disease (CAD), cerebrovascular disease, peripheral arterial disease or heart failure (HF) are automatically deemed to have a high 10-year risk.1 While the FRS is a useful clinical tool, it should be noted that it underestimates risk in certain categories of patients: in younger individuals, women, individuals with metabolic syndrome and those with chronic kidney disease.1 In addition, family history (evidence of CV disease in a first-degree relative younger than 60 years) is associated with a 1.7- and 2-fold increased risk of CV disease for women and men, respectively, which is not taken into account in the FRS. This factor helps to explain why 10% to 15% of patients with CAD have no apparent major CAD risk factors.1 As such, family history must be considered when assessing CV disease risk. Health professionals and patients may be much less concerned about individuals with low or moderate risk compared with those at high risk, and therefore may be less likely to recommend healthy lifestyle changes or medications. Moreover, in this population of patients, diagnoses such as angina may not be considered as often when the most remarkable complaints are symptoms such as excessive shortness of breath on exertion. This autobiographical case underscores the importance of heeding symptoms that may be suggestive of CV disease, regardless of risk assessment estimates. In addition, patients should be educated to appreciate the fact that low to moderate CV risk estimates do not imply that healthy lifestyle strategies are any less desirable.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".