Discordance between Framingham Risk Score and atherosclerotic plaque burden
Bibliographic record
Abstract
AIM: Clinical predictors are routinely used to identify individuals who may benefit from aggressive risk factor modification. However, clinical predictors cannot account for all genetic and environmental variables. The objective of this study is to investigate the association of Framingham Risk Score (FRS) with computed tomography angiography (CTA) measures of coronary atherosclerosis. METHODS AND RESULTS: Consecutive patients who underwent CTA were prospectively enrolled and categorized according to clinical predictors such as FRS and pre-test probability for obstructive coronary artery disease (CAD). Atherosclerotic calcific and non-calcific plaques were assessed. Of the 1507 patients without a history of diabetes mellitus, myocardial infarction, and not on statin therapy, coronary atherosclerosis was present in 63.5% of the patients. Of the 1173 patients with low and intermediate FRS, atherosclerotic plaque was visually present in 47.6 and 72.7% of the patients, respectively. A higher proportion of low FRS patients had isolated non-calcific plaque (14.8%) compared with patients in the intermediate (10.1%) or high (7.2%) FRS groups, and 11.7% of high FRS patients had no visual evidence of plaque. The correlation between FRS and plaque was fair (r = 0.48; P < 0.001). CONCLUSION: Although clinical variables are predictive of CAD events, CTA identified coronary atherosclerosis in a significant proportion of patients with low to intermediate FRS, and a small minority of patients with high FRS had no evidence of atherosclerosis. Prospective studies are required to determine the potential value of identifying coronary atherosclerosis using CTA and to assess whether modifying therapies based on these results are warranted.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".