Novel Biomarkers Do Not Correlate with Severity of Vascular Stiffness in CKD Patients with Severe Co-Morbid Disease
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Bibliographic record
Abstract
BACKGROUND/AIMS: Novel biomarkers may help explain the pathobiology of vascular disease in chronic kidney disease, and thus set the stage for identification of therapeutic targets, potential reversibility, and improved outcomes in this population. METHODS: 124 subjects with GFR <60 ml/min or on renal replacement therapy underwent measurement of inflammatory, vascular and cardiac biomarkers as well as aortic pulse wave velocity (PWV) testing. A subset of patients (n = 60) had repeat PWV measured at 6 months. RESULTS: Thirty-four percent of the patients were diabetic, and 50% had a history of cardiovascular disease or congestive heart failure. Median PWV was 9.8 (IQR 8.3-12.7) m/s. No significant correlations between the measured biomarkers and baseline PWV was observed. An increase in PWV (>1.5 m/s) over 6 months was observed in those subjects with diabetes, a higher brain natriuretic peptide level, lower cholesterol and lower phosphate level. Age (HR 1.086, p = 0.0028), fetuin (0.024, p = 0.0448), and interleukin-10 (top tertile HR 4.720, p = 0.0359) were associated with mortality. CONCLUSIONS: In this cohort of patients with chronic kidney disease and diabetes and/or heart disease, we were unable to demonstrate that select biomarkers can inform processes leading to vascular disease. Biomarkers do appear to have utility in predicting future events in this population.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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