Virtual reality technologies in multimodal rehabilitation for post-stroke pain: a randomized controlled trial
Bibliographic record
Abstract
Objective. To evaluate the impact of virtual reality technology on severity and characteristics of post-stroke pain syndrome (PSPS) and indicators of neurotrophin and neurotransmitter metabolism. Material and methods. Rehabilitation with multimodal stimulation was performed in 59 patients of the main group with PSPS (mean age 58.5±9.94 years, 38 (64.4%) men and 21 (35.6%) women). The control group included 38 patients with PSPS, in whose rehabilitation virtual reality training was not used (mean age 62.1±8.8 years, 21 men (55.3%) and 17 (44.7%) women). The virtual reality program “VRZdorovye” was used in rehabilitation of our patients after stroke. Pain syndrome was examined using the visual analogue scale, Douleur Neuropathique 4 (DN4), PainDetect, McGill pain questionnaire. We assessed serum concentration of neurotrophic factors (BDNF, NGF, VGF) and mediators (dopamine, serotonin, substance P, norepinephrine) using solid-phase enzyme immunoassay. Statistical analysis was carried out using the Statistica 12.0 software. Results. There was less severe pain, smaller number of patients with high risk of neuropathic pain, sensory and affective components of post-stroke pain syndrome in virtual reality group. There was higher serum serotonin and lower substance P after multimodal approach with virtual reality. Conclusion. Multimodal influence including virtual reality in complex medical rehabilitation of patients with post-stroke pain syndrome decrease pain syndrome. Changes in serum concentration of neurotrophic proteins and neurotransmitters in patients with post-stroke pain syndrome indicate possible effect of virtual reality on central pathogenetic mechanisms of post-stroke pain syndrome.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".