Association of Aspirin Resistance With Increased Stroke Severity and Infarct Size
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between aspirin resistance and clinical and neuroimaging measures of stroke severity in acute stroke patients. DESIGN: Prospective single-center survey of acute ischemic stroke patients receiving aspirin therapy. SETTING: The Royal Melbourne Hospital, Parkville, Victoria, Australia. PATIENTS: Ninety acute stroke patients who previously received aspirin therapy were enrolled. MAIN OUTCOME MEASURES: Clinical stroke severity was measured using the National Institutes of Health Stroke Scale (NIHSS) and stroke infarct size was measured using the Alberta Stroke Program Early CT Score (ASPECTS). Aspirin resistance was measured using the VerifyNow system. RESULTS: The mean (SD) age was 75 (9.9) years and 64.4% were male. The median NIHSS score and ASPECTS were 4 (interquartile range [IQR], 3-10) and 9 (IQR, 6-10), respectively. Aspirin resistance was detected in 28.9% (95% CI, 0.19 to 0.38) of all patients. The median aspirin reaction unit (ARU) was 486.0 (IQR, 432.3-557.0). Every 1-point increase in ARU was associated with a 0.03-point increase in NIHSS score (95% CI, 0.01 to 0.04; P<.001) and a 0.02-point decrease in ASPECTS (95% CI, -0.03 to -0.01; P<.001). This corresponded to an approximate median increase of 1 point in NIHSS score for every 33-point increase in ARU or a decrease of 1 point in ASPECTS for every 50-point increase in ARU. CONCLUSIONS: Aspirin resistance is associated with increased clinical severity and stroke infarct volume in acute stroke patients. Our results support the need for a randomized controlled study to investigate alternative antiplatelet therapy in patients with aspirin resistance.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".