N-terminal pro-brain natriuretic peptide improves the C-ACS risk score prediction of clinical outcomes in patients with ST-elevation myocardial infarction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: It remained unclear whether the combination of the Canada Acute Coronary Syndrome Risk Score (CACS-RS) and N-terminal pro-brain natriuretic peptide (NT-pro-BNP) could have a better performance in predicting clinical outcomes in acute ST-elevation myocardial infarction (STEMI) patients with primary percutaneous coronary intervention. METHODS: A total of 589 consecutive STEMI patients were enrolled. The potential additional predictive value of NT-pro-BNP with the CACS-RS was estimated. Primary endpoint was in-hospital mortality and long-term poor outcomes. RESULTS: The incidence of in-hospital death was 3.1%. Patients with higher NT-pro-BNP and CACS-RS had a greater incidence of in hospital death. After adjustment for the CACS-RS, elevated NT-pro-BNP (defined as the best cutoff point based on the Youden's index) was significantly associated with in hospital death (odd ratio = 4.55, 95%CI = 1.52-13.65, p = 0.007). Elevated NT-pro-BNP added to CACS-RS significantly improved the C-statistics for in-hospital death, as compared with the original score (0.762 vs. 0.683, p = 0.032). Furthermore, the addition of NT-pro-BNP to CACS-RS enhanced net reclassification improvement (0.901, p < 0.001) and integrated discrimination improvement (0.021, p = 0.033), suggesting effective discrimination and reclassification. In addition, the similar result was also demonstrated for in-hospital major adverse clinical events (C-statistics: 0.736 vs. 0.695, p = 0.017) or 3-year mortality (0.699 vs. 0.604, p = 0.004). CONCLUSIONS: Both NT-pro-BNP and CACS-RS are risk predictors for in hospital poor outcomes in patients with STEMI. A combination of them could derive a more accurate prediction for clinical outcome s in these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it