Relation between high density lipoprotein particles concentration and cardiovascular events: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Trails aimed at raising high density lipoprotein(HDL) cholesterol concentration failed to make better cardiovascular outcomes. HDL particles may be better biomarkers reflecting properties of HDL. This meta-analysis was conducted to evaluate the relation between blood HDL particles level and cardiovascular events. METHODS: PubMed and other databases were searched for eligible studies and NewCastle-Ottawa Quality Assessment Scale(NOS) was used to assess the quality of included studies. A random or fixed-effect model was applied to calculate the pooled hazard ratio(HR). RESULTS: Twelve studies were finally included. The pooled HR(95%confidence interval) for per standard deviation(SD) increment and top quartile versus bottom quartile were 0.79(0.72,0.86) and 0.65(0.57,0.75), respectively. Subgroup analysis suggested that HR was significantly lower in subjects with a cardiovascular disease(CVD) history than that of people without established CVD. Subclass analysis indicated that HRs for per SD increment of small(0.85) and medium(0.84) HDL particles were significantly lower than that of large HDL particles(0.96). CONCLUSIONS: HDL particle level in blood was inversely related to CVD events, indicating that HDL particles maybe a protective factor in patients with CVD, thus making HDL particles a potential biomarker and therapy target.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.023 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".