Plasma visfatin levels are associated with major adverse cardiovascular events in patients with acute ST-elevation myocardial infarction
Bibliographic record
Abstract
PURPOSE: Circulating levels of visfatin, a ubiquitous adipokine, may reflect both the severity of plaque as well as degree of plaque stabilization in acute myocardial injury. The purpose of this study was to test whether the level of visfatin is associated with the occurrence of major adverse cardiovascular events (MACEs) in patients with acute ST-elevation myocardial infarction (STEMI). METHODS: Consecutive patients (n=185) with acute STEMI were prospectively enrolled in the study. ELISA was used to measure plasma visfatin concentrations. Composite MACEs included death, recurrent myocardial infarction, target lesion revascularization or re-advanced heart failure. RESULTS: Plasma visfatin levels were significantly higher in composite MACE patients than in non-MACE patients. A multivariate Cox hazard regression model revealed that the predictive independent risk factors for the occurrence of composite MACEs were visfatin level (relative risk = 1.04) and age (relative risk = 6.05). When patients were grouped according to their plasma visfatin levels, composite MACEs occurred more frequently in patients presenting with high visfatin levels. Moreover, Kaplan-Meier analysis revealed that high visfatin levels were significantly associated with the occurrence of composite MACEs. CONCLUSIONS: The level of plasma visfatin may be associated with risk of composite MACEs in STEMI patients, and may be useful for risk stratification.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".