Meta-analysis of randomized controlled trials of the effectiveness of Prospekta in the treatment of vascular cognitive impairment of varying severity
Bibliographic record
Abstract
Objective. To conduct a meta-analysis of randomized controlled trials (RCTs) to evaluate the efficacy of Prospekta in the treatment of SCI of varying severity. Material and methods. The meta-analysis included the results of RCTs of the efficacy of Prospekta in the treatment of VCI, the severity of which was assessed using the Montreal Cognitive Scale (MoCA). The pooled effect estimate included all publications of double-blind, placebo-controlled RCTs that provided sufficient MoCA efficacy data to support further statistical analysis. The main result of the meta-analysis was obtained for the final values of the efficacy indicator in the groups of patients receiving the drug Prospekta, in comparison with the placebo group. Results. A meta-analysis of the effectiveness of Prospekta in the treatment of SCI of varying severity was carried out based on data from 3 RCTs and 2 CTs involving 12.701 patients aged 18 years and older. When using the mixed models method, the effect size for the endpoint «change in total MoCA score from baseline to follow-up visit» was 3.4 points for Prospekta (2.7 points for placebo, p<0.0001); for the end point «∆ between changes in the total score on the MoCA scale while taking Prospekta and placebo» — 0.6736 points (p<0.0001). Conclusion. A statistically significant improvement in cognitive function according to the MoCA scale was demonstrated in patients with VCI using the drug Prospekta.
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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 | 0.052 | 0.089 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.033 | 0.070 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".