Abstract P683: Thrombin Generation in Plasma of Stroke Patients With Atrial Fibrillation
Bibliographic record
Abstract
Background and Purpose: Thrombus formation is central to pathophysiology of stroke in patients with atrial fibrillation. Whether factors in plasma contribute to thrombus generation in patients with atrial fibrillation remains unclear. In this study we sought to determine whether plasma contributes to thrombin generation in patients with atrial fibrillation. Methods: There were 78 acute ischemic strokes with atrial fibrillation and 37 non-stroke controls. Plasma thrombin generation was measured by thrombin generation assay, resulting lag time, peak thrombin, time to peak and area under the curve was assessed. Thrombin generation capacity was compared in stroke patients with atrial fibrillation to non-stroke controls. The relationship to anticoagulation was assessed. In vitro, the effect of anticoagulation on plasma thrombin generation was determined. Results: Thrombin generation capacity was increased (shorter lag time and time to peak) in ischemic stroke patients with atrial fibrillation compared to non-stroke atrial-fibrillation controls (p<0.05 and p<0.01, respectively). Anticoagulation decreased plasma induced thrombin generation. Ischemic stroke patients with atrial fibrillation treated with anticoagulation (DOAC or warfarin) had lower plasma induced thrombin generation compared to atrial-fibrillation patients not on anticoagulation (p<0.05). Thrombin generation by plasma could be further reduced by DOAC in an in-vitro assay. Conclusions: Stroke patients with atrial fibrillation have a higher plasma induced thrombin generation compared to atrial fibrillation controls. Factors in plasma such as leukocyte derived tissue factor likely contribute to thrombus formation in patients with atrial fibrillation. As such, components in plasma may represent new targets to reduce thrombus formation and stroke risk in patients with atrial fibrillation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".