Atrial cardiopathy biomarkers and atrial fibrillation in the ARCADIA trial
Bibliographic record
Abstract
Abstract Background: ARCADIA compared apixaban to aspirin for secondary stroke prevention in patients with cryptogenic stroke and atrial cardiopathy. One possible explanation for the neutral result is that biomarkers used did not optimally identify atrial cardiopathy. We examined the relationship between biomarker levels and subsequent detection of AF, the hallmark of atrial cardiopathy. Methods: Patients were randomized if they met criteria for atrial cardiopathy, defined as P-wave terminal force >5000 μV*ms in ECG lead V1 (PTFV1), NT-proBNP >250 pg/mL, or left atrial diameter index (LADI) ⩾3 cm/m2. For this analysis, the outcome was AF detected per routine care. Results: Of 3745 patients who consented to screening for atrial cardiopathy, 254 were subsequently diagnosed with AF; 96 before they could be randomized and 158 after randomization. In unadjusted analyses, ln(NT-proBNP) (RR per SD, 1.99; 95% CI, 1.85–2.13), PTFV1 (RR per SD, 1.15; 95% CI, 1.03–1.28) and LADI (RR per SD, 1.34; 95% CI, 1.20–1.50) were associated with AF. In a model containing all 3 biomarkers, demographics, and AF risk factors, age (RR per 10 years, 1.24; 95% CI, 1.09–1.41), ln(NT-proBNP) (RR per SD, 1.88; 95% CI, 1.67–2.11) and LADI (RR per SD, 1.25; 95% CI, 1.14–1.37) were associated with AF. These three variables together had a c-statistic of 0.82 (95% CI, 0.79–0.85) but only modest calibration. Discrimination was attenuated in sensitivity analyses of patients eligible for randomization who may have been more closely followed for AF. Conclusions: Biomarkers used to identify atrial cardiopathy in ARCADIA were moderately predictive of subsequent AF.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".