Association of LDL-cholesterol subfractions with cardiovascular disorders: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disorders (CVDs) are the leading cause of death worldwide. This study aimed to evaluate the association between low-density lipoprotein (LDL) subfractions and cardiovascular disorders. METHODS: To ensure the rigor of the systematic review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used. For this systematic review, a comprehensive search strategy was performed in important databases including PubMed, Scopus, Embase, International Statistical Institute (ISI) Web of Science, and google scholar from 2009 to February 2021. The following terms were used for systematic search: low-density lipoprotein, LDL, subfractions, subclasses, nuclear magnetic resonance, NMR, chromatography, high-pressure liquid, HPLC, cardiovascular disease, cerebrovascular, and peripheral vascular disease. Also, for evaluating the risk of bias, the Newcastle-Ottawa scale was employed. RESULTS: At the end of the search process, 33 articles were included in this study. The results of most of the evaluated studies revealed that a higher LDL particle number was consistently associated with increased risk for cardiovascular disease, independent of other lipid measurements. Also, small dense LDL was associated with an increased risk of CVDs. There was no association between LDL subfraction and CVDs in a small number of studies. CONCLUSIONS: Overall, it seems that the evaluation of LDL subclasses can be used as a very suitable biomarker for the assessment and diagnosis of cardiovascular diseases. However, further studies are required to identify the mechanisms involved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it