Evaluation of association between atherogenic index of plasma and intima‐media thickness of the carotid artery for subclinic atherosclerosis in patients on maintenance hemodialysis
Bibliographic record
Abstract
Incidence of cardiovascular diseases in the patients having chronic kidney disease (CKD) is between 25% and 60%. This increased rate is proposed to be associated with "accelerated atherosclerosis." Increased carotid intima-media thickness (CIMT) is a subclinical atherosclerosis marker. Small-dense low-density lipoprotein particles are a strong risk factor for atherosclerosis. It was shown that atherogenic index of plasma (AIP = log(TG/HDL-c)) is correlated with size of the lipoprotein particles. We investigated the correlation between AIP and CIMT which is a subclinical atherosclerosis marker, in hemodialysis (HD) patients. A total of 62 persons with 31 patients under HD therapy and 31 volunteers were included in the study. In all the participants, CIMT was measured and AIP were calculated. AIP and CIMT values of the participants were compared with blood pressures, lipid profiles and the other risk factors. AIP (0.39 ± 0.32) and CIMT (0.57 ± 0.13) were found significantly higher in the patient group than in the controls (0.04 ± 0.36 and 0.45 ± 0.119, respectively); (P = 0.0001 and 0.0001, respectively). There was a significant correlation between AIP and increased CIMT in the patient group (P = 0.0001, r = 0.430). Among the lipid parameters, the strongest correlation was found between CIMT and AIP. We demonstrated the significant increase of AIP and CIMT in HD patients. A correlation was found between AIP and CIMT. AIP was found to show a correlation with a greater number of risk factors, both classical and CKD specific, than CIMT. These data suggest that AIP might be a method which can be used both in diagnosis of subclinical atherosclerosis and in deceleration processes of its progression.
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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 | 0.001 | 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".