Predictors of the efficacy of non-drug treatments for non-dementia vascular cognitive impairment
Bibliographic record
Abstract
Objective. To study the predictors of the efficacy of non-drug multimodal therapy in the treatment of mild vascular cognitive impairment. Material and methods. Thirty patients with mild vascular cognitive impairment, under the supervision of their physician, received a 1-month non-drug treatment program including cognitive training, detailed recommendations for physical activity, and dietary planning. Results. After the end of the course of treatment, improvements in the MoCa test were achieved by 22 patients (73%), which made up Group 1. In the remaining 8 patients, the treatment had no effect (Group 2). In Group 1, the dynamics of the MoCa test averaged 1.7±0.9, in the Group 2 it was (–0.4)±0.5. Patients of Group 1 had a significantly lower level of education (10.9±2.3) compared with Group 2 (14.9±2.0), a higher initial MoCa score, and a less pronounced white matter lesion on the Fazekas scale. After the regression analysis, the level of education (B –0.999, p<0.05) and white matter damage (B –2.761, p<0.01) were significant predictors. Conclusion. When using non-drug multimodal therapy in the treatment of mild vascular cognitive impairment, lower levels of education and a lower degree of white matter vascular damage are reliable predictors of treatment efficacy.
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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 | 0.001 | 0.005 |
| 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) | 0.002 | 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".