Exploring the Relationships between Hemoglobin, the Endothelium and Vascular Health in Patients with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND/AIMS: The ideal hemoglobin target in chronic kidney disease remains unknown. Ultimately, individualized targets may depend upon the properties of the patient's endothelial and vascular milieu, and thus the complex relationships between these factors need to be further explored. METHODS: Forty-six patients with a glomerular filtration rate (GFR) <30 ml/min/1.73 m(2) or on renal replacement therapy underwent measurement of hemoglobin, endothelial microparticles (EMPs) and aortic pulse wave velocity (PWV) at 0, 3 and 6 months. In addition, a number of inflammatory, cardiac and vascular biomarkers were measured at baseline. RESULTS: No correlation was observed between baseline values of PWV and EMPs, PWV and hemoglobin, or hemoglobin and EMPs in the overall cohort. When stratified by CKD status, a positive correlation was observed between PWV and EMP CD41-/CD144+ in patients with GFR <30 ml/min/1.73 m(2) only (r = 0.54, p = 0.01). Asymmetric dimethylarginine correlated with baseline PWV (r = 0.27, p = 0.07), and remained significantly correlated with the 3- and 6-month PWV measurement. CONCLUSIONS: In this small heterogeneous cohort of dialysis and non-dialysis patients, we were unable to describe a physiologic link between anemia, endothelial dysfunction and arterial stiffness.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".