Biomarkers in kidney and heart disease
Bibliographic record
Abstract
There is much symptomatic similarity between acute kidney disease and acute heart disease. Both may present with shortness of breath and chest discomfort, and thus it is not surprising that biomarkers of acute myocardial and renal disease often coexist in many physicians' diagnostic work-up schedules. In this review we explore the similarities and differences between current and future tests of myocardial and renal injury and function, with particular emphasis on the diagnostic utility of currently available biomarkers to assist with the diagnosis of cardiorenal syndromes. Imaging studies have not traditionally been viewed as clinical biomarkers, but as tests of structure and function; they contribute to the diagnostic process, and we believe that they should be considered alongside more traditional biomarkers such as blood and urine measurements of circulating proteins and metabolites. We discuss the place of natriuretic peptides, novel tests of kidney damage as well as kidney function and conclude with a discussion of their place in guiding future research studies whose goals must include better characterization of the degree of dysfunction imposed on one organ system by failure of the other.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".