Signatures of cardioembolic and large‐vessel ischemic stroke
Bibliographic record
Abstract
OBJECTIVE: The cause of stroke remains unknown or cryptogenic in many patients. We sought to determine whether gene expression signatures in blood can distinguish between cardioembolic and large-vessel causes of stroke, and whether these profiles can predict stroke etiology in the cryptogenic group. METHODS: A total of 194 samples from 76 acute ischemic stroke patients were analyzed. RNA was isolated from blood and run on Affymetrix U133 Plus2.0 microarrays. Genes that distinguish large-vessel from cardioembolic stroke were determined at 3, 5, and 24 hours following stroke onset. Predictors were evaluated using cross-validation and a separate set of patients with known stroke subtype. The cause of cryptogenic stroke was predicted based on a model developed from strokes of known cause and identified predictors. RESULTS: A 40-gene profile differentiated cardioembolic stroke from large-vessel stroke with >95% sensitivity and specificity. A separate 37-gene profile differentiated cardioembolic stroke due to atrial fibrillation from nonatrial fibrillation causes with >90% sensitivity and specificity. The identified genes elucidate differences in inflammation between stroke subtypes. When applied to patients with cryptogenic stroke, 17% are predicted to be large-vessel and 41% to be cardioembolic stroke. Of the cryptogenic strokes predicted to be cardioembolic, 27% were predicted to have atrial fibrillation. INTERPRETATION: Gene expression signatures distinguish cardioembolic from large-vessel causes of ischemic stroke. These gene profiles may add valuable diagnostic information in the management of patients with stroke of unknown etiology though they need to be validated in future independent, large studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".