Circulating levels of tumor necrosis factor alpha, brain natriuretic peptide and cardiac Troponin I upon admission and 31-day mortality in patients with acute decompensated chronic heart failure
Bibliographic record
Abstract
Elevated circulating levels of TNFα, brain natriuretic peptide (BNP) and cardiac Troponin I (cTnI) have been connected with adverse prognosis in patients with chronic heart failure (CHF). However, there are scant data about the predictive value of these biomarkers in combination. A total of 577 consecutive patients (mean age: 73 ± 9 years), who were hospitalized for acute decompensation of NYHA class III/IV (65.3% of ischemic etiology) low-output (mean LVEF: 22 ± 5) CHF, were studied. Biochemical markers were measured upon admission. The incidence of 31-day death was the prespecified primary endpoint. The incidence of the primary endpoint was 17.7%. By multivariate Cox analysis, including baseline characteristics and the study biomarkers, elevated circulating levels of TNFα (RR = 2.1; P < 0.001), BNP (RR = 3.5; P < 0.001) and cTnI (RR = 3.8; P < 0.001) were independently associated with the primary endpoint. When the patients were divided according to the number of positive biomarkers (estimated by ROC analysis) there was a significant gradual increase in the rate of the primary endpoint with increasing of the number of the positive biomarkers (4.1%, 10%, 21.5% and 53.5% 31-day mortality rate for patients with zero, one, two and three positive biomarkers, respectively; P < 0.001) (Figure 1 ). abstract The present results suggest that in patients hospitalized due to acutely decompensated severe low-output CHF, serum levels of TNFα, BNP and cTnI can be used in combination for enhanced early risk stratification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".