Impact of clinical presentation and presence of coronary sclerosis on long-term outcome of patients with non-obstructive coronary artery disease
Bibliographic record
Abstract
BACKGROUND: Non-obstructive coronary artery disease (NOCAD) is a common finding on coronary angiography. Our goal was to evaluate the long-term prognosis of NOCAD patients with stable angina (SA). METHODS: The study cohort consisted of 7478 NOCAD patients with normal EF (≥ 50%), and SA who underwent coronary angiography between 1995 and 2012. We compared NOCAD patients (stenosis< 50%) with 10,906 patients with stable obstructive CAD (≥ 50%). The primary endpoint was all-cause mortality. Secondary endpoints included repeat angiography, progressive CAD, and PCI. A second comparison group consisted of 7344 patients with NOCAD presenting with an ACS. Rates of all-cause mortality of NOCAD ACS patients were compared to NOCAD SA patients. RESULTS: Median follow-up time was 6.5 years. NOCAD patients had a lower risk of all-cause mortality compared to CAD patients (HR CAD vs. NOCAD 1.33 (1.19-1.49); p < 0.001). This was driven by patients with normal coronary arteries (HR CAD vs. normal 1.63 (1.36-1.94), p < 0.001), whereas patients with minimal disease (> 0% and < 50%) were at similar risk as CAD patients (HR CAD vs. minimal 1.08 (0.99-1.29), p = 0.06). In NOCAD patients, the strongest predictors of all-cause mortality were age and minimal disease. SA patients with NOCAD had low rates of repeat angiography (7.3%), future CAD (2.3%) and PCI (1.7%). NOCAD ACS patients had a 41% increase in all-cause mortality risk compared to NOCAD SA patients (HR 1.41 (1.25-1.6), p < 0.001). CONCLUSIONS: This study underlines the importance of minimal CAD, as it is not a benign disease entity and portends a similar risk as stable obstructive CAD.
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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 | 0.001 |
| 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".