Assessment of total carotid plaque area progression in patients with chronic kidney disease. Good practices for decision-making
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) increases cardiovascular risk, however, traditional cardiovascular risk factors cannot entirely explain it. A real-world investigation examined the concept that renal function decline is linked to carotid total plaque area progression, which strongly confirms cardiovascular risk. We analyzed CKD patients in stages 1-3 to find risk factor relationships before the onset of severe CKD. METHODS: ). Ultrasound-guided total plaque area tracked atherosclerosis. Age, sex, blood pressure, lipids, and HbA1c were covariates. Total plaque area and variables were measured on day 1 and at the conclusion of observation. We used a multilevel mixed effects model to assess biological and behavioral factors on total plaque area progression in the general population. For validation, this research was conducted on 73 CKD patients with optimal traditional cardiovascular risk factor management during 15 ± 5 months. RESULTS: Multiple analyses showed an inverse relationship between eGFR decline and total plaque area progression [β-exponent = 0.99 (95% CI = 0.98-0.99)], regardless of age, lipid profile, blood pressure, smoking, diabetes, or hypertension. The correlation remained significant in the 73-patient sample with optimal traditional cardiovascular risk factor management (β-exponent = 0.99; 95% CI 0.97-0.99). Although traditional cardiovascular risk factor management was excellent, total plaque area increased considerably in G2-G3 patients compared to G1. CONCLUSIONS: CKD, total plaque area, and eGFR are inversely correlated, independent of traditional cardiovascular risk factors, suggesting that non-traditional mechanisms are responsible for resistant atherosclerosis. The combination of eGFR and total plaque area may be useful in identifying high-risk patients.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".