Effects of Serum Cholesterol on Severity of Stroke and Dosage of Statins on Functional Outcome in Acute Ischemic Stroke
Bibliographic record
Abstract
Background: A high dose of statin is used to obtain an intensive lipid-lowering in stroke patients, even in patients with normal lipid levels. There are limited data on effect of dosage of statins and functional outcome in stroke patients. Objectives: To compare serum cholesterol levels with severity of stroke measured by infarct volume. To compare functional outcome measured by mRS at day 90 with the dose of statin. Materials and Methods: This retrospective observational study was conducted in KMC Hospital Manipal, India between 2016 and 2018. Result: A total of 100 consecutive patients were included in the study, out of which 60 (60.0%) were males. Hyperlipidemia was present in 65 (65.0%) patients. On comparing the serum cholesterol levels with infarct volume using MRI, patients with low volume of ≤70 ml had higher mean serum total cholesterol concentration (223.83 mg/dl), whereas patients with high volume of >70 ml had low mean cholesterol level (218.70 mg/dl). The patients were divided into those who received low dose (≤20 mg) versus high dose (≥40 mg equivalent) of Atorvastatin. On comparing the mRS values at baseline and on day 90 with the dose of statins, patients who received a higher dosage had a statistically significant fall in mRS (p-0.045) at day 90. Conclusion: It was found that serum cholesterol levels were inversely related to the stroke severity. However, a higher the dose of statins resulted in better functional outcome and survival in post-stroke patients, possibly due to its neuroprotective effect.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".