Influence of Noninvasive Cardiovascular Imaging in Primary Prevention
Bibliographic record
Abstract
BACKGROUND: Despite extensive use in practice, the impact of noninvasive cardiovascular imaging in primary prevention remains unclear. METHODS: We searched for randomized trials that compared imaging with usual care and reported any of the following outcomes in a primary prevention setting: medication prescribing, lifestyle modification (including diet, exercise, or smoking cessation), angiography, or revascularization. RESULTS: Seven trials were included. Trials screened patients for inducible myocardial ischemia (2 trials), coronary calcification (3 trials), carotid atherosclerosis (1 trial), or left ventricular hypertrophy (1 trial). Imaging had no effect on medication prescribing overall (odds ratio [OR], 1.01; 95% confidence interval [CI], 0.76-1.33) or on provision of lipid-modifying agents (OR, 1.08; 95% CI, 0.58-2.01), antihypertensive drugs (OR, 1.05; 95% CI, 0.75-1.47), or antiplatelet agents (OR, 1.05; 95% CI, 0.84-1.32). Similarly, no effect was seen on dietary improvement (OR, 0.78; 95% CI, 0.22-2.85), physical activity (0.02 vs -0.08 point change for imaging vs control on a 5-point scale; P = .23), or smoking cessation (OR, 2.24; 95% CI, 0.97-5.19). Imaging was not associated with invasive angiography (OR, 1.26; 95% CI, 0.89-1.79). CONCLUSIONS: We found limited evidence suggesting that noninvasive cardiovascular imaging alters primary prevention efforts. However, given the imprecision of these results, further high-quality studies are needed.
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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 | 0.040 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".