Apixaban Versus Aspirin to Reduce Cognitive Decline After Cryptogenic Stroke and Atrial Cardiopathy: ARCADIA‐Cognition Study
Bibliographic record
Abstract
Background ARCADIA (Atrial Cardiopathy and Antithrombotic Drugs in Prevention After Cryptogenic Stroke), a secondary stroke prevention study comparing apixaban versus aspirin for cryptogenic stroke and biomarkers of atrial cardiopathy, ended prematurely for futility. In the ARCADIA‐Cognition study we hypothesized that cognitive decline would be slower in the apixaban arm due to less microembolization. Methods ARCADIA subjects on study drug and eligible for magnetic resonance imaging participated in ARCADIA‐CSI (ARCADIA‐Cognition and Silent Infarction). Cognitive tests were administered centrally by telephone ≥3 months after the ARCADIA index stroke and yearly thereafter. Composite Z scores were the means of 5 standardized cognitive tests. Trajectories of the composite Z score and the individual test scores were compared between treatment arms using a mixed‐effects model. Results Of 799 screened patients at 75 sites, 310 were enrolled in ARCADIA‐CSI. Of these, 296 completed at least 1 cognitive exam, and 47 subjects were excluded from the primary analysis for baseline dementia. For the 249 subjects included in the analysis, there were 582 cognitive assessments. Baseline characteristics were balanced between the apixaban (n=128) and aspirin (n=121) arms. Mean age was 68 (SD: 10.4) years, median modified Rankin Scale score 1 (interquartile range, 0–2), 52% female, and 19% Black. During median follow‐up of 378 (interquartile range, 183–735) days, the annual change in the overall standardized composite score was 0.084 (95% CI, 0.017–0.149) in the aspirin arm and 0.107 (95% CI, 0.041–0.174) in the apixaban arm ( P =0.62). Conclusions Cognitive trajectories did not differ between apixaban and aspirin. Further studies should address infarct location and volume and concurrent pathology to determine optimal treatment to mitigate cognitive decline with atrial disease. Registration URL: http://clinicaltrials.gov ; Unique Identifier: NCT03192215.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".