Reduced Heart Function Predicts Drug-Taking Compliance and Two-Year Prognosis in Chinese Patients With Stable Premature Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine the association between heart function, compliance with drug administration, and the mid-term prognosis in Chinese patients with stable premature coronary artery disease (CAD) (male < 55 years and female < 65 years). METHODS: The study included 512 patients with stable premature CAD. An estimated glomerular filtration rate (eGFR) calculated using the MDRD formula, baseline clinical characteristics, use of medications for coronary secondary prevention therapies (aspirin, β-blocker, angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers, or statins), and 2-year follow-up results, in particular major adverse cardiac events (MACEs), were collected and analyzed. RESULTS: Patients with reduced left ventricular ejection fraction (LVEF) (18.75%) were more prevalent among men, smokers, those with type 2 diabetes, with a family history of cardiovascular disease (CVD), and with higher white blood cells counts ((8.88 ± 0.35) × 10(9)/L vs. (6.90 ± 0.17) × 10(9)/L) (all P < 0.05) compared to those with preserved LVEF. There was no significant difference between creatinine or eGFR values in the two groups with reduced and preserved LVEF (all P > 0.05). Patients with LVEF < 50% in the MACEs group had a lower ratio of optimal drug administration compared to the MACEs-free group (Z = -0.228, P = 0.820 and Z = -2.167, P = 0.03 respectively). Patients with reduced LVEF had a significantly higher ratio of composite MACEs than patients with preserved LVEF during 2-year follow-up (47.13% vs. 33.50%, P < 0.05). CONCLUSIONS: Stable premature CAD patients with reduced LVEF have more risk factors, lower medication compliance, and worse 2-year outcomes than those with preserved LVEF.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".