Comparative effects of clinic- and virtual reality-based McKenzie extension therapy in chronic non-specific low-back pain
Bibliographic record
Abstract
Purpose The study compared the influence of Clinic-Based McKenzie Therapy (CBMT) and a Virtual Reality Game (VRG) version on pain intensity, back extensor muscles endurance, activity limitation, participation restriction, fear avoidance belief, kinesiophobia, and general health status of patients with chronic non-specific low-back pain. Methods This quasi-experimental study involved 46 patients (CBMT: n = 24; VRG: n = 22) with ‘directional preference’ for extension, randomized into CBMT or VRG group. Treatment was applied thrice weekly for 8 weeks. Outcomes were assessed at the end of the 4th and 8th week. Data analysis employed descriptive and inferential statistics of independent t-test, Mann-Whitney U test, repeated measure ANOVA, Friedman’s ANOVA, and ANCOVA. The significance level was set as α = 0.05. Results There were no significant differences in the treatment outcomes (mean change) across the groups (p > 0.05), except for kinesiophobia, where VRG led to a significantly higher decline in mean rank at week 4 (28.3 vs. 19.1; p = 0.018) and 8 (28.7 vs. 18.7; p = 0.009), and vitality (a general health status item) at week 4 (27.6 vs. 19.8; p = 0.042) and 8 (28.1 vs. 19.3; p = 0.042). ANCOVA showed that significant baseline parameters were not significant predictors of vitality (F = 1.986; p = 0.070) or kinesiophobia (F = 0.866; p = 0.563) outcomes. Conclusions The VRG mode of McKenzie therapy is comparable with the clinic-based approach in most outcomes. VRG has a superior effect on kinesiophobia, but may take a higher toll on vitality/energy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".