Cardiovascular Biomarkers in Chronic Kidney Disease: State of Current Research and Clinical Applicability
Bibliographic record
Abstract
The high incidence of cardiovascular events in chronic kidney disease (CKD) warrants an accurate evaluation of risk aimed at reducing the burden of disease and its consequences. The use of biomarkers to identify patients at high risk has been in use in the general population for several decades and has received mixed reactions in the medical community. Some practitioners have become staunch supporters and users while others doubt the utility of biomarkers and rarely measure them. In CKD patients numerous markers similar to those used in the general population and others more specific to the uremic population have emerged; however their utility for routine clinical application remains to be fully elucidated. The reproducibility and standardization of the serum assays are serious limitations to the broad implementation of these tests. The lack of focused research and validation in randomized trials rather than ad hoc measurement of multiple serum markers in observational studies is also cause for concern related to the clinical applicability of these markers. We review the current literature on biomarkers that may have a relevant role in field of nephrology.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".