Influence of Standardized Extract Ginkgo biloba EGb761 ® Towards Quality of Life Indicators in Patients with Diabetes Mellitus Type 2
Bibliographic record
Abstract
Cognitive impairment in patients with type 2 diabetes mellitus (DM-2) currently attracts a lot of attention due to their impact on quality of life and the effectiveness of treatment. The aim of research is to find the most effective medication which influences the cognitive functions positively. The research included 120 patients with average age of 61.22 ± 8.6 and average DM-2 duration of 10.84 ± 8.2 years. Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA test), Trail Making Test (TMT), Parts A and B, Hospital Anxiety and Depression Scale (HADS), and The Short Form-36 (SF-36) were used. It was revealed that patients with DM type 2 had cognitive dysfunction generally presented by mild cognitive impairment. Patients with DM-2 have an early manifestation of cognitive impairment. After the initial estimation of indicators, all the patients were taking standardized extract Ginkgo biloba (EGb 761®) in the dose 240 mg a day for 6 months. Estimation of all the indicators after 3 and 6 months of treatment showed significant cognitive improvement. By matching the available experimental and clinical data, we can conclude that in the setting of DM-2 EGb 761®, by producing a positive effect towards various factors which results from insulin resistance of the brain, improves functions of the brain, which manifests in improvement of main QoL indicators in DM-2.
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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 | 0.000 | 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) | 0.001 | 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".