A Multidimensional Virtual Reality Neurorehabilitation Approach to Improve Functional Memory: Who Is the Ideal Candidate?
Bibliographic record
Abstract
Aims: We aimed to identify the significant predictors of ecological memory amelioration after the Human Empowerment Aging and Disability (HEAD) rehabilitation program, a multidimensional treatment for chronic neurological diseases. Materials and Methods: Ninety-three patients with Parkinson disease (n = 29), multiple sclerosis (n = 26), and stroke (n = 38) underwent a multidimensional rehabilitation. We focused on changes after treatment on ecological memory (outcome measure) evaluated by Rivermead Behavioral Memory Test, Third Edition (RBMT-3). Minimal clinically important difference (MCID) after treatment were calculated for RBMT-3. The change score on RBMT-3 was categorized in positive effect, stabilization, or no effect of the treatment. Random forest classification identified who significantly benefited from treatment against who did not in terms of ecological memory functioning. Accordingly, logistic regression models were created to identify the best predictors of the treatment effect. A predicted probability value was derived, and the profile of the ideal candidate of HEAD protocol was shown by combining different ranks of significant predictors in a 3 × 3 matrix for each pair of predictors. Results: A significant number of cases reported positive effect of the treatment on ecological memory, with an amelioration over the MCID or a stabilization. The random forest analysis highlighted a discrete accuracy of prediction (>0.60) for all the variables considered at baseline for identifying participants who significantly benefited and who did not from the treatment. Significant logistic regression model (Wald method) showed a predictive role of Montreal Cognitive Assessment (MoCA; p = 0.007), 2-Minute Walk Test (2MWT; p = 0.038), and RBMT-3 (p < 0.001) at baseline on HEAD treatment effect. Finally, we observed a high probability of success in people with higher residual cognitive functioning (MoCA; odds ratio = 1.306) or functional mobility (2MWT; odds ratio = 1.013). Discussion: The HEAD program is a rehabilitation with effects on multiple domains, including ecological memory. Residual level of cognitive and/or motor functioning is a significant predictor of the treatment success. These findings confirm the intrinsic relationship subsisting between motor and cognitive functions and suggest the beneficial effects of physical activity on cognitive functions and vice versa.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".