The Prognostic Value of Cystatin C and Urinary NGAL in Patients With the Cardiorenal Syndrome
Bibliographic record
Abstract
Congestive heart failure (CHF) is a common disease and leads to numerous deaths in Canada annually. Part of the reason is due to the high prevalence of renal dysfunction in this patient population, a comorbidity that acts as an independent risk factor for the progression of cardiovascular disease and therefore dramatically increases morbidity and mortality. The phenomenon linking the interaction between these two vital organ systems is called the cardiorenal syndrome. Further understanding of the pathophysiology as well as the ability to predict the development of cardiorenal syndrome in patients with CHF can aid clinicians in guiding therapy towards prevention, and optimizing patient management. To further the understanding of this disease, as well as risk stratify patients with CHF it is important to look at novel biomarkers like serum cystatin C and urinary neutrophil gelatinase-associated lipocalin (NGAL). Cystatin C has been shown to be a robust measure of renal function as it is not subjected to many of the limitations that exist for creatinine. NGAL is a marker of renal tubular injury and levels in both urine and serum are quickly increased when the renal tubules are damaged. By looking at the predictive value and pattern of both of these biomarkers in the development and progression of renal dysfunction in ambulatory CHF patients, we can identify individuals with greater risks of adverse events as well as further our understanding in the development of the cardiorenal syndrome.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".