Effect of Probucol and Atorvastatin Combination Therapy on Cognitive Function in Patients with Acute Ischemic Stroke: A Clinical Trial Study
Bibliographic record
Abstract
Abstract Background In this study, we aimed to investigate the effect of probucol combined with atorvastatin on cognitive impairment after infarction in patients with acute ischaemic stroke and to compare the evaluation methods of cognitive impairment. Patients and methods: A total of 81 patients with acute ischaemic stroke admitted to the Affiliated Hospital of Chengde Medical College between November 2020 and May 2021 were enrolled in this study. Using a random number table method, they were divided into probucol combined with atorvastatin (n = 40) and atorvastatin (n = 41) groups. Cognitive function (Montreal Cognitive Assessment) scores and blood lipid levels were assessed six months after treatment and compared between the two groups. Results Approximately 60.5% (49/81) of patients in the acute phase of stroke experienced cognitive decline. After six months, 39.5% (32/81) of the patients improved from baseline, with 27.5% (11/40) in the combined therapy group and 51.2% (21/41) in the atorvastatin group presenting with cognitive impairment. Patients with cognitive impairment after six months were significantly different between the two groups (t = 4.766, P = 0.029). Serum total cholesterol and low-density lipoprotein (LDL) levels decreased significantly (P < 0.05). Additionally, there was no statistically significant difference in the common carotid artery intimal thickness and plaque area (P > 0.05) between the treatment groups. In terms of factors affecting cognition, the multivariate generalised estimating equations suggested a statistically significant difference in terms of previous cerebrovascular history, measurement stage, combination therapy, infarct area, and LDL levels (P < 0.05). Conclusion Probucol combined with atorvastatin can significantly improve post-stroke cognitive function and quality of life in patients with acute ischaemic stroke and is safe, feasible, and worthy of clinical promotion. Trial registration: Chinese Clinical Trial Registry (ChiCTR2000040461) registed time:2020-11-28
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".