Plasma parameters and risk factors of patients with post-stroke cognitive impairment
Bibliographic record
Abstract
BACKGROUND: It is high of the incidence of stroke and dementia with the advent of an aging society. Post-stroke cognitive impairment is one of the common complications of stroke, which not only seriously affects the life quality of patients, but also significantly reduces the survival time of stroke patients. Moreover, it also brings in heavy burden to the family and society. The development of vascular dementia could be reduced by early intervention after stroke. Management of vascular risk factors could be an effective way to prevent dementia. This study aimed to investigate the plasma biochemical parameters of post-stroke cognitive impairment (PSCI) and its potential risk factors. METHODS: Four hundred eighty-seven consecutive patients with ischaemic stroke were included and followed up for 3 years. Among these patients, 132 cases were diagnosed as PSCI. The cognitive impairment of patients with PSCI was assessed by the Mini Mental State Examination and Montreal cognitive assessment scale. The plasma biochemical parameters and blood coagulation, as well as computed tomography and magnetic resonance imaging of all the patients after admission, were measured. RESULTS: Multivariate analyses revealed that increased age, carotid plaque, cerebral atrophy, white matter lesions (WML), alcohol use, smoking and history of systolic blood pressure ≥170 mmHg was highly associated with PSCI (P<0.05). Elevated homocysteine, low-density lipoprotein (LDL), and uric acid were also highly associated with PSCI. Logistic regression analysis identified five risk factors correlated with PSCI including alcohol use [odds ratio (OR): 5.138, 95% confidence interval (CI): 1.014-26.04, P=0.048], history of high systolic blood pressure (OR: 12.171, 95% CI: 3.339-44.363, P=0.001), carotid plaque (OR: 1.692, 95% CI: 1.032-2.796, P=0.040), cerebral atrophy (OR: 2.280, 95% CI: 1.294-4.001, P=0.004), and WML (OR: 3.155, 95% CI: 1.868-5.324, P=0.001). Three plasma biochemical parameters were also associated with PSCI including homocysteine (OR: 1.018, 95% CI: 0.944-1.042, P=0.010), and LDL (OR: 0.83, 95% CI: 0.6-1.148, P=0.051), and uric acid (OR: 1.00, 95% CI: 0.998-1.002, P=0.007). The area under the receiver operating curve for the risk factors of PSCI was 0.821 with the sensitivity of 76.3% and specificity of 71.9%. CONCLUSIONS: Elevated homocysteine, LDL, and uric acid were highly related to PSCI, which may help predict PSCI. These plasma biochemical parameters together with vascular risk factors, may improve the sensitivity for early detection of PSCI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".