Relationship between serum N-terminal Pro Brain Natriuretic Peptide (NT-Pro BNP) level and the severity of coronary artery involvements.
Bibliographic record
Abstract
BACKGROUND: Rapid measuring of B-type natriuretic peptide (BNP) in the emergency departments effectively results in evaluating patients with acute cardiac attacks and has appeared to be a useful prognostic marker of cardiovascular risk. A current study came to address the association between plasma N-terminal pro BNP level and severity of coronary vessels' defects based on Gensini score in patients with stable angina pectoris candidate for coronary angiography. METHODS: The study population consisted of 92 consecutive patients with appearance of stable angina and candidate for coronary angiography. All participants underwent selective left and right coronary angiography. For BNP measurement and just before the catheterization of left coronary, 5cc blood samples were drawn from coronary. RESULTS: With respect to the role of N terminal pro BNP for predicting severity of CAD based on Gensini scoring, linear regression analysis confirmed that plasma BNP level was a strong predictor for CAD severity (p = 0.009) in the presence of study cofounders. A significant correlation was also observed between N terminal pro BNP and left ventricular ejection fraction, so that all patients with left ventricular dysfunction (EF < 40%) had plasma N terminal pro BNP level higher than 100 pg/ml. CONCLUSIONS: NT-pro BNP can be a good parameter for predicting the severity of coronary vessels' involvement besides other diagnostic tools. In all patients with left ventricular ejection fraction less than 40%, plasma NT-pro BNP level was higher than 100 pg/ml.
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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 | 0.000 | 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) | 0.002 | 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".