Plasma ProBNP Is Not a Specific Marker for Transient Myocardial Ischemia
Bibliographic record
Abstract
BACKGROUND: Plasma proBNP levels are increased in patients with acute myocardial infarction. Previous studies have shown conflicting data on the effect of transient myocardial ischemia on plasma BNP levels. We designed the current study to examine plasma proBNP levels in patients with transient myocardial ischemia during a percutaneous coronary intervention (PCI). This study was to study plasma proBNP as a marker of transient myocardial ischemia. METHODS: We enrolled 49 consecutive patients with a history of angina or abnormal stress test who presented for cardiac catheterization. We obtained plasma proBNP levels in all patients at 1) arterial access (proBNP-1), 2) the end of the procedure (proBNP-2) and 3) 4 hours after procedure (proBNP-3). Hotelling's T-squared test was used to evaluate the equality of means. Log transforms of proBNP were used to impart data normality. RESULTS: Twenty-two patients underwent diagnostic catheterization (DCA group) and 27 underwent PCI (PCI group). Both groups had normal left ventricular function and a baseline creatinine < 2 mg/dL. Baseline log (proBNP) was 4.7 + 0.99 (units) and rose significantly at 4 hours in both groups (P < 0.02), with no difference in rate of change. CONCLUSIONS: Plasma proBNP was increased in both DCA and PCI groups which limits its utility to identify transient myocardial ischemia. The etiology of increase in proBNP in both groups is speculative and may be related to injection of radiographic contrast media into the coronary artery which leads to microcirculatory impairment resulting in myocardial tissue hypoxia and transient increase in left ventricular pressure; however, further evaluation is required.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".