Prevalence of obstructive coronary artery disease and prognosis in patients with stable symptoms and a zero-coronary calcium score
Bibliographic record
Abstract
AIMS: CT calcium scoring (CTCS) and CT cardiac angiography (CTCA) are widely used in patients with stable chest pain to exclude significant coronary artery disease (CAD). We aimed to resolve uncertainty about the prevalence of obstructive coronary artery disease and long-term outcomes in patients with a zero-calcium score (ZCS). METHODS AND RESULTS: Consecutive patients with stable cardiac symptoms referred for CTCS or CTCS and CTCA from chest pain clinics to a tertiary cardiothoracic centre were prospectively enrolled. In those with a ZCS, the prevalence of obstructive CAD on CTCA was determined. A follow-up for all-cause mortality was obtained from the NHS tracer service. A total of 3914 patients underwent CTCS of whom 2730 (69.7%) also had a CTCA. Half of the patients were men (50.3%) with a mean age of 56.9 years. Among patients who had both procedures, a ZCS was present in 52.2%, with a negative predictive value of 99.5% for excluding ≥70% stenosis on CTCA. During a mean follow-up of 5.2 years, the annual event rate was 0.3% for those with ZCS compared with 1.2% for CS ≥1. The presence of non-calcified atheroma on CTCA in patients with ZCS did not affect the prognostic value (P = 0.98). CONCLUSION: In patients with stable symptoms and a ZCS, obstructive CAD is rare, and prognosis over the long-term is excellent, regardless of whether non-calcified atheroma is identified. A ZCS could reliably be used as a 'gatekeeper' in this patient cohort, obviating the need for further more expensive tests.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".