Biochemical Aspirin Resistance Affect on Stroke Severity in Acute Stroke Patients Who Had Taken Aspirin
Bibliographic record
Abstract
Background: There are conflicting data in the literature regarding aspirin resistance. This study evaluated the effect of biochemical aspirin resistance on initial stroke severity in acute stroke patients who had taken aspirin. Methods: We reviewed acute ischemic stroke patients who were already on aspirin. Biochemical aspirin resistance was defined as an aspirin reaction unit score of ≥550, as evidenced by the VerifyNow-Aspirin assay, which was performed after 4 days of continuous aspirin medication. Initial stroke severity was evaluated using National Institutes of Health Stroke Scale (NIHSS) scores at day 4, which were dichotomized into mild (0-7) and severe (≥8). Modified Rankin Scale scores were determined at 3 months. The Alberta Stroke Program Early CT Scores (ASPECTS) were assessed on initial diffusion-weighted imaging (DWI). We examined the relationships between biochemical aspirin resistance and initial stroke severity. Results: Nine of 106 patients (8.5%) had biochemical aspirin resistance. The initial stroke severity was significantly associated with DWI-ASPECTS (p<0.001), initial C-reactive protein level (p=0.005), biochemical aspirin resistance (p =0.009), and stenosis or occlusion of the relevant artery (p=0.029). Multivariate analysis showed that biochemical aspirin resistance [odds ratio (OR), 15.24; 95% confidence interval (CI), 2.49-93.31; p=0.003] and initial C-reactive protein level (per 1 mg/dL; OR, 2.43; 95% CI, 1.47-4.00; p=0.001) were independently associated with initial stroke severity (NIHSS score ≥8). However, biochemical aspirin resistance was not associated with clinical outcome at 3 months (p=0.366). Conclusions: Biochemical aspirin resistance was independently associated with initial stroke severity. This suggests that detection of biochemical aspirin resistance in acute ischemic stroke is useful when choosing the optimal treatment. J Korean Neurol Assoc 29(4):303-308, 2011
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".