Virtual reality in the correction of pain syndrome in patients with degenerative-dystrophic joints and spine diseases
Bibliographic record
Abstract
Musculoskeletal system diseases require active motor rehabilitation, as a rule, but presence of severe pain syndrome might become a barrier, leading to the development of kinesiophobia and reducing motivation for treatment in patients. In recent decades, non-invasive methods of pain control, in particular virtual reality (VR) and augmented reality (AR) have been commonly used on a par with drug therapy. The purpose of this study is to provide a scientific base for the effectiveness of including a high-tech VR device (Vive Focus Plus EEA Virtual Reality Helmet), in to psychological rehabilitation of a pain syndrome in patients with chronic degenerative-dystrophic diseases of major joints and spine. The study involved 84 patients (24 men and 60 women aged 56±14.4) of a rehabilitation hospital with a severe pain syndrome and motor disorders corresponding to ICF Class 1 or 2. To analyse the characteristics of the subjective pain perception, the method of multidimensional semantic description based on the adapted Russian version of the McGill Pain Questionnaire was applied, and the Tampa Scale was used to kinesiophobia assessment. The VR technology was implemented via usage of the Vive Focus Plus EEA Virtual Reality Helmet tool (10 procedures). The effectiveness of using VR technology was evaluated through monitoring of pain dynamics and the kinesiophobia level prior to the study onset and at the end of hospitalization. As a result, the study has shown that there was no nosological specificity in the description of pain, or the differences in its verbal characteristics representing nociceptive and neuropathic components. Technology of ‘virtual immersion in 3D reality’ makes it possible to influence effectively on pathophysiological mechanisms links in the development of chronic psychologically determined, neuropathic and mixed-origin pain.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".