Reply to: N-terminal pro brain natriuretic peptide in coronary artery disease
Bibliographic record
Abstract
In response to the letter by Professor Sim Sai Tin, we agree that the clinical usefulness of NT-proBNP has been widely discussed. Dr. Sim Sai Tin mentions the report by Ranjith et al. [1] on the usefulness of this biomarker in which the authors suggest that “NT-proBNP should be included in the risk assessment of ACS to provide guidance for further therapeutic strategies”. I would first like to state that the discussion of related subjects from various centers is mandatory and that multiple perspectives and the study of diverse demographics add to the enrichment of medical knowledge. Furthermore, our study was different to the one conducted by Ranjith et al. in several ways. First, our study populations were dissimilar. Our patients comprised those with unstable angina (48.48%; 64 patients) and NSTEMI (34.8%; 46 patients) while patients with STEMI represented only 16.6% (22 patients). On the other hand, most of the patients in the study by Ranjith et al. had STEMI (71%; 142 patients). Second, we performed serial assessments of NT-proBNP on admission and after cardiac catheterization; while Ranjith et al. assessed NT-proBNP only on admission. Third, coronary angiography was conducted on all patients in our study and we assessed the severity of coronary artery lesion, while Ranjith et al. conducted the same on only a small percentage (21% of his patients). And finally, most of our patients were treated by revascularization either by percutaneous coronary intervention (PCI) or surgical revascularization in addition to medical treatment, while the patients in the study by Ranjith et al. were treated medically in most cases, with cardiac catheterization performed on only 21% of their patients. Finally, our study concluded that NT-proBNP is not only a prognostic marker for complications and poor prognosis in acute coronary syndrome, but that it can also predict the severity of coronary artery stenosis and the number of vessels affected [2]. Written by Abdelhakem Selem Elsayed.
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.
How this classification was reachedexpand
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 | 0.002 | 0.005 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".