P4316N-terminal pro-B-type natriuretic peptide (NT-proBNP) is associated with stroke among hospitalized medical patients: an APEX trial substudy
Bibliographic record
Abstract
Background: Stroke risk stratification targeting hospitalized medical patients remains a challenge. Purpose: The study aimed to assess the predictive value of N-terminal pro-B–type natriuretic peptide (NT-proBNP) for stroke and its impact on the treatment effect of betrixaban. Methods: In the APEX trial, 7,513 hospitalized medical patients were randomized to receive betrixaban (80 mg od for 35–42 days) or enoxaparin (40 mg od for 10±4 days) for VTE prevention. NT-proBNP was analyzed by the central laboratory at baseline. The association of NT-proBNP levels and other risk factors with stroke was assessed at 77 days after randomization. The rate of stroke was compared between treatment groups stratified by NT-proBNP. Results: Baseline NT-proBNP was measured in 4,542 hospitalized patients (median: 1,525 ng/L; IQR: 429–4,759 ng/L). NT-proBNP was both a univariate and a multivariate correlate of stroke (Table). Compared to enoxaparin, betrixaban was associated with a significant stroke reduction among patients with elevated NT-proBNP (0.69% vs 2.09%; RR=0.33 [95% CI: 0.13–0.83]; P=0.013; NNT=72). There was an absolute risk difference of 1.40% using 2,750 ng/L as the cutoff, a cutoff that was independently associated with stroke at 77 days (adjusted OR=3.51 [95% CI: 1.63–7.54]; P=0.0013).
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".