New possibilities for the treatment of mild cognitive impairment and prevention of dementia in patients with cerebrovascular disease. Results of the PRIORITET Observation Program
Bibliographic record
Abstract
A common tool for detecting mild cognitive impairment (MCI) is the Montreal Cognitive Assessment (MoCA). An extension of the standard MoCA testing – MoCA-Memory Index Score (MoCA-MIS; Memory Index, MI) – allows a deeper study of the mechanisms of MCI formation. We consider the drug Prospecta as an effective measure to prevent the progression of MCI. Objective : to study the efficacy and safety of Prospecta in the treatment of MCI in patients with cerebrovascular disease (CVD) in real clinical practice. Material and methods . The observational program PRIORITET included 192 patients of both sexes aged 40 to 74 years inclusive (mean age 63.6±8.8 years) with MCI and CVD, they were prescribed Prospecta, two tablets twice a day during 24 weeks. The presence of MCI was confirmed by testing on the MoCA scale; MI was assessed additionally`. At the end of treatment, the condition of patients was assessed using the Overall Clinical Impression Scale and the safety of the therapy. Results . The change in the mean score on the MoCA scale after 24 weeks of therapy with Prospecta was 5.3 (p<0.0001). The proportion of patients who responded positively to treatment with Prospecta and after 24 weeks of treatment restored their cognitive functions to normal (MoCA >26 points), reached 48.9%. Additionally, patients were divided into groups depending on the results of testing on the MoCA scale and MI. During therapy with Prospecta, the number of patients in the MIS <7 and MoCA <20 group decreased by 19 times, in the MIS <7 or MoCA <20 group – by 4.8 times. There were 16 adverse events, 75% of which were mild. In 62.5% of adverse events, there was no connection with taking Prospecta. Conclusion . Prospecta is an effective and safe treatment for patients with MCI and CVD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".