The relationship between lipoprotein(a) and coronary artery disease, as well as its variable nature following myocardial infarction
Bibliographic record
Abstract
PURPOSE: The present study aimed to investigate the relationship between the severity of coronary artery disease (CAD) and level of Lipoprotein (LP)(a). METHODS: The study included 52 CAD patients and a control group consisting of 38 individuals. The patients were classified into three groups based on the clinical form of CAD (stable angina pectoris, SAP, unstable angina pectoris, UAP, and myocardial infarction,MI), and were further divided into three groups based on CAD severity (1-, 2- and 3-vessel). Serum Lp(a) levels were monitored 4, 8, and 24 h, 10 and 30 days following acute MI in 18 patients. RESULTS: Based on regression analysis, Lp(a) was not correlated with other lipoproteins or with risk factors of CAD, such as body mass index, smoking, family history, diabetes, age, gender, and hypertension (r = 0.08-0.22). 72% of the patients in the CAD group and 24% of the control group had an Lp(a) level > 30 mg dL(-1) (P = 0.004), and Lp(a) levels were higher in 3-vessel patients than in 2-vessel and 1-vessel CAD patients (86% vs. 68%, P = 0.02 and 86% vs. 62%, P=0.01, respectively). Serum Lp(a) levels were higher in the UAP and MI groups than in the SAP group (48 ± 44.7 mg dL(-1), 49 ± 36.1 mg dL(-1) and 31.2 ± 22.3 mg dL(-1), respectively, P=0.02). Lp(a) levels increased after acute MI, and reached peak levels 10 days post-MI (41% increase, P=0.001) and remained considerably elevated (18%) 30 days post-MI (P=0.01). CONCLUSION: Serum Lp(a) was higher in the UAP and MI patients in comparison with the SAP patients, and was higher in 3-vessel CAD in comparison with 1- and 2-vessel CAD patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".