Effects of chronic kidney disease and declining renal function on coronary atherosclerotic plaque progression: a PARADIGM substudy
Bibliographic record
Abstract
AIMS: To investigate the change in atherosclerotic plaque volume in patients with chronic kidney disease (CKD) and declining renal function, using coronary computed tomography angiography (CCTA). METHODS AND RESULTS: In total, 891 participants with analysable serial CCTA and available glomerular filtration rate (GFR, derived using Cockcroft-Gault formulae) at baseline (CCTA 1) and follow-up (CCTA 2) were included. CKD was defined as GFR <60 mL/min/1.73 m2. Declining renal function was defined as ≥10% drop in GFR from the baseline. Quantitative assessment of plaque volume and composition were performed on both scans. There were 203 participants with CKD and 688 without CKD. CKD was associated with higher baseline total plaque volume, but similar plaque progression, measured by crude (57.5 ± 3.4 vs. 65.9 ± 7.7 mm3/year, P = 0.28) or annualized (17.3 ± 1.0 vs. 19.9 ± 2.0 mm3/year, P = 0.25) change in total plaque volume. There were 709 participants with stable GFR and 182 with declining GFR. Declining renal function was independently associated with plaque progression, with higher crude (54.1 ± 3.2 vs. 80.2 ± 9.0 mm3/year, P < 0.01) or annualized (16.4 ± 0.9 vs. 23.9 ± 2.6 mm3/year, P < 0.01) increase in total plaque volume. In CKD, plaque progression was driven by calcified plaques whereas in patients with declining renal function, it was driven by non-calcified plaques. CONCLUSION: Decline in renal function was associated with more rapid plaque progression, whereas the presence of CKD was not.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".