Early administration of high-dose IV unfractionated heparin blunts sVCAM-1 and improves functional outcome more effectively than 300 mg oral aspirin in acute ischemic stroke.
Bibliographic record
Abstract
P186 Methods: We studied prospectively 167 patients with ischemic stroke (58% males, 67.7± 9.8 years) that received 1000 IU/h i.v. unfractionated heparin (UFH) (n=70) or 300 mg oral aspirin (n=97) at a mean treatment delay of 6.7 hours (95% CI 5.9–7.4). Baseline and 48 hours plasma levels of IL-6, IL-10, IL-4, TNF-α, sICAM-1, and sVCAM-1 were compared in both treatment groups. All patients had baseline and follow up CT scans, and stroke diagnosis followed TOAST criteria. Multivariate analyses adjusting for confounders assessed the impact of cytokines and adhesion factors on the Barthel Index (BI) score at 6 months. Results: Whereas TNF-α, and sICAM-1 decreased at follow-up, IL-6, IL-4, and sVCAM-1 increased compared to baseline values (all p<0.01). sVCAM-1 levels, adjusted for baseline inflammatory levels, neurological impairment, and CT findings were significantly lower at follow-up in patients treated with UFH (p=0.01). Other inflammatory markers did not differ between aspirin and UFH. Independent predictors of poor outcome (BI < 85) included 5 mm Hg increase in MAP (OR 1.16, 1.01–1.33), early CT signs of infarction (OR 8.30, 2.8–24.64), 1°C increase in body temperature (OR 3.23, 1.58–6.6), 1 point decrease in the Canadian Stroke Scale (OR 1.66, 1.31–2.12), and a twofold increase at 48 hours of baseline sVCAM-1(OR 2.19,1.1–4.39). Conclusions: The rise of sVCAM-1 is an independent predictor of clinical outcome at 6 months in patients with ischemic stroke. sVCAM-1 is more effectively decreased with high-dose UFH than with aspirin. To confirm that shorter heparin delay, IV route, and high-dose are required to blunt inflammation and improve functional outcome more effectively than aspirin in ischemic stroke a multinational trial has been organized, the Rapid Anticoagulation Prevents Ischemic Damage (RAPID) study.
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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) | 0.001 | 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) | 0.001 | 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".