Abstract WP234: Metabolite Profiling Identifies a Specific Profile for Cardioembolic Stroke
Bibliographic record
Abstract
Introduction: Accurate determination of stroke etiology is important in order to implement appropriate secondary stroke prevention. Approximately 25% are embolic strokes of undetermined source, a subset of which are eventually attributed to cardioembolism after prolonged outpatient electrocardiogram monitoring. We applied metabolite profiling to determine whether a metabolomic profile could identify a cardioembolic signature that could inform stroke of undetermined source. Hypothesis: We hypothesized that a specific metabolomic profile reflected by circulating metabolites could be derived from patients with stroke due to cardioembolism. Methods: Using liquid chromatography-tandem mass spectrometry, we analyzed 153 metabolites measured in plasma samples collected within 9 hours of stroke onset. Three hundred twenty six patients from an acute stroke cohort collected at two institutions were analyzed. Stroke subtypes were assigned using the Causative Classification System. Association with cardioembolic subtype was assessed and metabolites exceeding the Bonferroni corrected p value threshold were identified. Logistic regression was used to identify independent metabolite predictors of cardioembolic stroke. Results and Conclusion: The following metabolites were significant in univariate analysis: 2-hydroxyglutaric acid, glutathione, glyceric acid, hippuric acid, threonine, ornithine, histidine, cotinine, pseudouridine, C18:2-carnitine, tricarboxylic acid metabolites, and tryptophan pathway metabolites (all p<6.5x10-3). Regression analysis identified the following multivariate predictors of cardioembolic stroke: hippuric acid (p=0.045), ornithine (p=6x10-5), histidine (p=4x10-6), and a composite sum of tricarboxylic acid metabolites (p=0.004). Metabolite profiling may augment traditional approaches to diagnosing cardioembolic stroke and may have implications for the diagnosis of embolic stroke of undetermined source.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".