Utility of <scp>TG</scp>/<scp>HDL</scp>‐c ratio as a predictor of mortality and cardiovascular disease in patients with chronic kidney disease undergoing hemodialysis: A systematic review
Bibliographic record
Abstract
The triglyceride/high-density cholesterol-lipoprotein (TG/HDL-c) is a biomarker of cardiovascular events and mortality. In hemodialysis patients, the evidence is controversial. A systematic review was carried out in the Medline, Scopus, Embase, Web of Science, and Pubmed databases to identify the relevant cohort studies on cardiovascular events and mortality in hemodialysis patients the role of TG/HDL-c as a risk factor. Four cohort-type studies were evaluated, with a total of 52,579 hemodialysis patients. Three studies conducted in Asian populations and one study in the United States had the highest percentage of the sample (50,673 patients). The elevated TG/HDL-c ratio is associated with better survival, and there is a consistent gradual inverse association between TG/HDL-c and mortality in all analysis subgroups. In the decile categorization of the exposure variable, a 21% decrease in the risk of cardiovascular mortality and a 15% decrease in all-cause mortality in the highest decile compared to the reference group (D10 aHR = 0.79; 95% CI: 0.69-0.91 and D10 aHR = 0.85; 95%CI: 0.78-0.92). Our results show that the TG/HDL-c ratio is a protective factor for cardiovascular outcomes and mortality in the American population and a risk factor for them in the population from Asia.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".