The Role of Biomarkers in Predicting Cognitive Impairment in Elderly Patients with Heart Failure
Bibliographic record
Abstract
OBJECTIVE: This study explores the impact of sST2, Growth Differentiation Factor 15 (GDF-15), and clinical factors on cognitive dysfunction in elderly patients with heart failure with reduced ejection fraction (HFrEF). METHODS: A cohort of 101 chronic stable HFrEF patients aged over 65 years old participated in the study. Cognitive functions were assessed using the Montreal Cognitive Assessment (MoCA) test and the Mini Mental State Examination (MMSE). Levels of sST2, GDF-15, and N-terminal pro b-type natriuretic peptide (NT-proBNP) were also measured. RESULTS: Notably higher levels of NT-proBNP and GDF-15 were observed in the group with cognitive dysfunction, whereas sST2 levels were similar between the groups. The cognitive dysfunction group consisted of older patients. A higher proportion of patients with normal cognitive function had received influenza vaccinations. Furthermore, GDF-15 levels inversely correlated with MMSE score. Right ventricular diameter was negatively correlated, while hemoglobin levels were positively correlated with both MoCA and MMSE scores. Logistic regression analysis identified increased GDF-15 levels, older age, and advanced New York Heart Association (NYHA) classes as predictors of higher cognitive dysfunction risk, whereas influenza vaccination was linked to a reduced risk of cognitive dysfunction. CONCLUSION: Cognitive dysfunction in elderly patients with heart failure may be influenced by factors such as age, right ventricular enlargement, anemia, NYHA functional class, and levels of GDF-15 and NT-proBNP.
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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) | 0.001 | 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 | 0.000 |
| 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".