Guiding Therapy by Coronary CT Angiography Improves Outcomes in Patients With Stable Chest Pain
Bibliographic record
Abstract
BACKGROUND: Within the SCOT-HEART (Scottish COmputed Tomography of the HEART Trial) trial of patients with stable chest pain, the use of coronary computed tomography angiography (CTA) reduced the rate of death from coronary heart disease or nonfatal myocardial infarction (primary endpoint). OBJECTIVES: This study sought to assess the consistency and mechanisms of the 5-year reduction in this endpoint. METHODS: In this open-label trial, 4,146 participants were randomized to standard care alone or standard care plus coronary CTA. This study explored the primary endpoint by symptoms, diagnosis, coronary revascularizations, and preventative therapies. RESULTS: Event reductions were consistent across symptom and risk categories (p = NS for interactions). In patients who were not diagnosed with angina due to coronary heart disease, coronary CTA was associated with a lower primary endpoint incidence rate (0.23; 95% confidence interval [CI]: 0.13 to 0.35 vs. 0.59; 95% CI: 0.42 to 0.80 per 100 patient-years; p < 0.001). In those who had undergone coronary CTA, rates of coronary revascularization were higher in the first year (hazard ratio [HR]: 1.21; 95% CI: 1.01 to 1.46; p = 0.042) but lower beyond 1 year (HR: 0.59; 95% CI: 0.38 to 0.90; p = 0.015). Patients assigned to coronary CTA had higher rates of preventative therapies throughout follow-up (p < 0.001 for all), with rates highest in those with CT-defined coronary artery disease. Modeling studies demonstrated the plausibility of the observed effect size. CONCLUSIONS: The beneficial effect of coronary CTA on outcomes is consistent across subgroups with plausible underlying mechanisms. Coronary CTA improves coronary heart disease outcomes by enabling better targeting of preventative treatments to those with coronary artery disease. (Scottish COmputed Tomography of the HEART Trial [SCOT-HEART]; NCT01149590).
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".