Association between serum non-high-density lipoprotein cholesterol and cognitive dysfunction after acute ischemic stroke: a cross-sectional study
Bibliographic record
Abstract
This study aimed to explore the association between serum non-high-density lipoprotein cholesterol (non-HDL-C) and cognitive dysfunction risk in patients with acute ischemic stroke (AIS). This cross-sectional study enrolled 583 AIS patients. Biochemical markers and lipid profile were collected after admission. AIS patients were classified into high group (non-HDL-C ≥3.4 mM) and normal group (non-HDL-C <3.4 mM). Mini-Mental State Examination scale (MMSE), Montreal Cognitive Assessment scale (MoCA), Activities of Daily Living (ADL) scale, Neuropsychiatric Inventory (NPI), and Hamilton Depression scale 21 version (HAMD-21) were applied on the third day after admission. Compared with the control group, patients of the high group had higher body mass index and higher frequency of intracranial artery stenosis, and exhibited higher levels of non-HDL-C, total cholesterol, triglycerides, low-density lipoprotein cholesterol, homocysteine, fasting blood glucose, and glycosylated hemoglobin (HbA1c), and lower levels of high-density lipoprotein cholesterol (all P<0.05). Compared with the control group, patients of the high group had significantly lower MMSE and MoCA scores (MMSE: 26.01±4.17 vs 23.12±4.73, P<0.001; MoCA: 22.28±5.28 vs 20.25±5.87, P<0.001) and higher scores on the NPI and HAMD-21 (both P<0.001). MMSE (r=-0.306, P<0.001) and MoCA scores (r=-0.251, P<0.001) were negatively associated with non-HDL-C level. Multivariate regression analysis revealed that non-HDL-C level (OR=1.361, 95%CI: 1.059-1.729, P=0.016) was independently associated with the presence of cognitive dysfunction after adjusting for confounding factors. High serum non-HDL-C level might significantly increase the risk of cognitive dysfunction after AIS.
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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 | 0.001 |
| 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".