Immersive VR movement visualization in patients with hemophilic knee arthropathy: randomized, multicenter, single-blind clinical trial
Bibliographic record
Abstract
PURPOSE: To evaluate the efficacy of an immersive movement visualization intervention in patients with hemophilia and hemophilic knee arthropathy. MATERIALS AND METHODS: Randomized, single-blind clinical study. Twenty-eight patients with hemophilia were recruited. Patients were randomized to an experimental group (four weeks of immersive movement visualization) and a control group (no intervention). The intensity of pain, pressure pain threshold in the knee, tibialis anterior muscle, lower back level, conditioned pain modulation, range of knee motion, and lower limb functionality were evaluated. RESULTS: = 0.003). 42.86% of the patients in the experimental group exhibited changes greater than the minimum detectable change (MDC) in functionality. 39.29% of the patients subject to the intervention experienced changes greater than the MDC in the knee pressure pain threshold. CONCLUSIONS: Immersive motion visualization can improve the intensity of joint pain and functionality in patients with hemophilic knee arthropathy. Functionality, pressure pain threshold, and pain intensity improved in those patients who conducted immersive movement visualization.Implications for rehabilitationImmersive visualization of movement significantly improves intensity of joint pain, functionality, pressure pain threshold, joint health, and conditioned pain modulation in patients with hemophilic knee arthropathy.The fact that it is a therapy without potential aversive stimuli makes it a possible access pathway for patients with high levels of kinesiophobia and/or catastrophism.This low-cost, home-based technology allows its use in patients far from hemophilia reference centers or with difficult access to physiotherapy treatments.The immersive visualization of movement influences the democratization of treatment, in accordance with the WHO's Sustainable Development Goal 3 (health and well-being for all).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".