Effectiveness of ginkgo diterpene lactone meglumine on cognitive function in patients with acute ischemic stroke
Bibliographic record
Abstract
Abstract Objective To explore the efficacy of ginkgo diterpene lactone (GDLM) on cognitive function in patients with acute ischemic stroke (AIS). Methods A total of 126 patients with AIS in Shaanxi Provincial People’s Hospital from July 2019 to December 2020 were collected and randomly divided into the control group and treatment group (n = 63). All patients received conventional treatment, on which 25 mg/day GDLM was administered in the treatment group. Coagulation and inflammation indexes, National Institutes of Health Stroke Scale (NIHSS) and activities of daily living scale (ADL) scores were measured before and 14 days after treatment. NIHSS and ADL scores were performed again after 3 months. Cognitive function was assessed by Montréal Cognitive Assessment (MoCA) score, Mini-Mental State Examination (MMSE) score, and potential P300. Results After 14 days of treatment, all biochemical indices were lower than before treatment (P < 0.05). The NIHSS and ADL scores of the treatment group were significantly better than those of the control group after treatment (P < 0.05). The MoCA and MMSE scores of the treatment group improved more significantly compared with the control group (P < 0.05). After treatment, the P300 indexes of both groups were significantly better than before treatment (P < 0.05). Conclusion Conventional treatment of AIS combined with GDLM can effectively improve the cognitive function of patients, which is worthy of clinical recommendation.
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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 | 0.000 |
| 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 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".