Cost effectiveness of virtual reality game compared to clinic based McKenzie extension therapy for chronic non-specific low back pain
Bibliographic record
Abstract
Background: Low-back pain (LBP) is a major public health problem globally and its direct and indirect healthcare costs are growing rapidly. Virtual reality involving the use of video games or non-game applications are alternatives to conventional face-to-face physical therapy for LBP. The purpose of this study was to assess the cost-effectiveness of Back Extension-Virtual Reality Game (BE-VRG) compared to Clinic-based McKenzie therapy (CBMT) for chronic non-specific LBP in Nigeria. Methods: Patients with chronic non-specific LBP were randomised into either BE-VRG or CBMT group. Patients' level of disability was assessed using Oswestry Disability Index (ODI) at week 4 and week 8. ODI was mapped to SF-6D to generate quality adjusted life years (QALYs) used for cost-effectiveness analysis. Resource use and costs were assessed based on rehabilitation services from a healthcare perspective. Cost-effectiveness analysis which included direct healthcare costs was conducted. Incremental cost per QALY was also calculated. Results: = 24) with the mean (±SD) age of 32.6 ± (11.5) years for BE-VRG and 48.8 ± (10.2) years for CBMT intervention completed in this study. The mean direct health costs per patient were USD100.67 and USD106.3 for BE-VRG and CBMT, respectively. The mean quality adjusted life years at week 4 and week 8 were (BE-VRG, 0.0574 ± (0.002); CBMT, 0.0548 ± (0.002)); and (BE-VRG; 0.116 ± (0.002); CBMT; 0.114 ± (0.004)), respectively. Incremental cost-effectiveness ratio showed that BE-VRG arm was less costly and more effective than CBMT. Conclusion: The findings of this study suggest that BE-VRG was cost saving for chronic non-specific LBP compared to CBMT. This evidence could guide policy makers, payers and clinicians in evaluating BE-VRG as a treatment option for people with chronic non-specific LBP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 teacher head, 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".