Association among tenascin-C and NT-proBNP levels and arrhythmia prevalence in heart failure
Bibliographic record
Abstract
Purpose Tenascin-C (TN-C) and amino-terminal fragment of the B-type natriuretic peptide (NT-proBNP) are the important predictors in prognosis of heart failure (HF). The aim of this study was to analyze the relationship of TN-C and NT-proBNP levels with the frequency and severity of ventricular arrhythmia. Materials and Methods Our study included 107 HF patients with EF < 45%. According to Holter analysis, the patients were divided into two groups as malignant arrhythmia group (n=29) with Lown Class 4a and 4b arrhythmia and benign arrhythmia group(n=78) with Lown Class 0-3b arrhythmia. The groups were compared with respect to levels of TN-C and NT-proBNP. The relationship of TN-C and NT-proBNP levels with frequency of ventricular premature beat (VPB) was also analyzed. Findings NT-proBNP (5042.1±1626 versus 1417.1±1711.6 pg/ml) and TN-C (1089±348.6 versus 758.5±423.9 ng/ml) levels were significantly higher in the malignant arrhythmia group than that of the benign arrhythmia group (p<0.001). A significantly strong positive correlation (r=0.741, p<0.001) was found between the NT-proBNP levels and VPB numbers of the patients, whereas a significantly weak positive correlation was detected between the TN-C levels and VPB numbers of the patients (r=0.347, p<0.001). Conclusion This study has shown that NT-proBNP and TN-C levels increase in the HF patients with malignant arrhythmia. In the HF patients; the frequency of ventricular arrhythmias also increases as NT-proBNP and TN-C levels increase. However, in this study, the old NT-pro BNP seems better than the TN-C.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".