Effect of serum lipid levels on cerebral stroke severity and outcomes after intravenous thrombolysis
Bibliographic record
Abstract
Abstract Background Hyperlipidemia has been suggested to influence the severity and outcomes of acute ischemic stroke (AIS). However, its role in AIS patients receiving recombinant tissue plasminogen activator (rtPA) remains controversial. Intravenous thrombolysis has become standard practice in eligible patients with acute ischemic stroke. Understanding the relationship between lipid levels and post-thrombolysis outcomes could help in risk stratification and clinical decision-making. We conducted a prospective observational cohort of 50 adults with AIS who received intravenous rtPA within 4.5 h of onset and had baseline Alberta stroke programme early computed tomography score (ASPECTS) greater than 6, indicating limited early ischemic change. Patients were categorized by lipid profile status, and outcomes were compared between hyperlipidemic and normolipidemic groups. We assessed stroke severity using National Institutes of Health Stroke Scale (NIHSS), functional outcomes using Modified Rankin Scale (mRS), and ischemic burden using ASPECTS. Results Hyperlipidemia was present in 62% of patients and correlated with greater stroke severity P < 0.001), poorer 3-month functional outcomes ( P < 0.001), and larger ischemic cores ( P < 0.001). Follow-up CT showed a significantly higher frequency of middle cerebral artery M1 infarctions in hyperlipidemic patients ( P = 0.038). The multivariate regression indicates that triglycerides level was independently associated with admission NIHSS ( P < 0.001), followed by HDL-C ( P < 0.001). Conclusion Hyperlipidemia is correlated with more severe neurological deficits, poorer functional outcomes, and more extensive ischemic changes on neuroimaging in patients with acute ischemic stroke treated with rtPA. Acute lipid profiling may serve as a practical tool for risk stratification in thrombolyzed stroke patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".