Relationship Between Neutrophil Count and 90‐Day Outcomes and Effect of Dual Antiplatelet Therapy in Patients With Acute Ischemic Stroke or Transient Ischemic Attack: A Post Hoc Analysis of the INSPIRES Trial
Bibliographic record
Abstract
BACKGROUND: Inflammation is an important mechanism in ischemic stroke and high-risk transient ischemic attack, but clinical inflammatory markers on antiplatelet therapy remain to be studied. This study was designed compare the neutrophil count (NC) on the efficacy and safety of clopidogrel-aspirin with that of aspirin in patients with ischemic stroke or high-risk transient ischemic attack caused by intracranial or extracranial atherosclerosis. METHODS: The INSPIRES (Intensive Statin and Antiplatelet Therapy for High-Risk Intracranial or Extracranial Atherosclerosis) study was a post hoc analysis of the multicenter, randomized, double-blind, placebo-controlled, 2-by-2 factorial trial. The primary efficacy and safety outcomes were 90-day stroke and moderate-to-severe bleeding. The differences in the efficacy outcome were calculated with cox proportional hazards model and the generalized linear model as well as logistic regression. RESULTS: /L). Patients with ischemic stroke or transient ischemic attack with a higher NC benefited more from clopidogrel-aspirin than from aspirin alone. There was no significant difference in the primary safety outcome of moderate-to-severe bleeding according to antiplatelet therapy or NC. CONCLUSIONS: The post hoc analysis suggested patients with a higher NC obtained greater benefit from clopidogrel-aspirin than from aspirin without an increase in bleeding risk. The findings may serve as a reference indicator for future anti-inflammatory therapy. However, further research is needed to explore the mechanism.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".