Comparative effects of clinic- and virtual reality-based McKenzie extension therapy in chronic non-specific low-back pain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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: <i>n</i> = 24; VRG: <i>n</i> = 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 4<sup>th</sup> and 8<sup>th</sup> 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 (<i>p</i> > 0.05), except for kinesiophobia, where VRG led to a significantly higher decline in mean rank at week 4 (28.3 vs. 19.1; <i>p</i> = 0.018) and 8 (28.7 vs. 18.7; <i>p</i> = 0.009), and vitality (a general health status item) at week 4 (27.6 vs. 19.8; <i>p</i> = 0.042) and 8 (28.1 vs. 19.3; <i>p</i> = 0.042). ANCOVA showed that significant baseline parameters were not significant predictors of vitality (F = 1.986; <i>p</i> = 0.070) or kinesiophobia (F = 0.866; <i>p</i> = 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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) | 0.000 | 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 it