Association between serum non-high-density lipoprotein cholesterol and cognitive impairment in patients with acute ischemic stroke
Bibliographic record
Abstract
BACKGROUND: Non-high density lipoprotein cholesterol (HDL-C) could be a good predictor of vascular disease outcomes. To evaluate the association between serum non-HDL-C and cognitive impairment in patients with acute ischemic stroke. METHODS: A total of 725 hospitalized patients with acute ischemic stroke were enrolled. They received conventional treatment. Cognitive function was assessed on the 3rd day after admission using mini-mental state examination (MMSE), Montreal Cognitive Assessment (MoCA), Activity of Daily Living Scale (ADL), and Neuropsychiatric Inventory (NPI, and Hamilton depression rating scale 21-item (HAMD-21). Lipid profile and biochemical markers were measured, and non-HDL-C was calculated. RESULTS: Compared with patients with normal non-HDL-C, those with high non-HDL-C showed lower MMSE (23.1 ± 4.9 vs. 26.0 ± 4.6, P < 0.001) and MoCA (20.4 ± 6.4 vs. 22.2 ± 5.3 P = 0.01) scores, higher NPI (6.2 ± 1.2 vs. 3.3 ± 1.5, P < 0.001) and HADM-21 (6.0 ± 2.2 vs. 4.5 ± 1.9, P < 0.001) scores, and higher homocysteine (16.0 ± 3.8 vs. 14.3 ± 2.0 mmol/L, P = 0.044), fasting blood glucose (6.4 ± 2.7 vs. 6.1 ± 2.1 mmol/L, P = 0.041), and HbA1c (6.80 ± 1.32 % vs. 6.52 ± 1.17 %, P = 0.013) levels. MMSE (r = -0.526, P < 0.001), MoCA (r = -0.216, P < 0.001), and NPI (r = 0.403, P < 0.001) scores were correlated with non-HDL-C levels. High non-HDL-C levels were an independent risk factor for cognitive disorders after acute ischemic stroke (P = 0.034, odds ratio = 3.115, 95 % confidence interval: 1.088-8.917). CONCLUSIONS: High serum non-HDL-C levels, age, education, homocysteine levels, and HAMD score were independent risk factors of cognitive impairment in patients with acute ischemic stroke. The risk of cognitive disorders after acute ischemic stroke increased with increasing non-HDL-C levels. This parameter is easy to assess in the clinical setting.
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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) | 0.000 | 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) | 0.001 | 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".