Effects of an Intensive 6-Week Rehabilitation Program with the HUBER Platform in the Treatment of Non-Specific Chronic Low Back Pain: A Pilot Study
Bibliographic record
Abstract
Non-specific chronic low back pain (NSCLBP) is defined as a complex disorder involving structural, biomechanical, cognitive, psychological, social, and lifestyle factors. Non-pharmacological approaches such as exercise and physical therapy have been proposed in first-line treatments, along with psychological follow-up and pain medication if needed. Our objective was to evaluate the effectiveness of an intensive rehabilitation program with HUBER (a multi-axis motorized platform equipped with force sensors, allowing patients to perform physical exercises in an isometric mode) on the spine flexion-to-extension ratio at 60 and 120°/s, pain, and trunk flexibility in individuals with NSCLBP. Twelve participants underwent a clinical evaluation including isokinetic spine strength and participated in a 6-week rehabilitation program with HUBER 360 Evolution. The main findings of this pilot study show that the flexor/extensor ratios at 60°, the flexibility of the hamstring and quadriceps, and muscular endurance of the trunk, disability, and quality of life were significantly improved at the end of the rehabilitation program (p < 0.05). Low back pain and analgesic medication were also reduced. Exercising with the HUBER Platform seems to be effective in managing NSCLBP but a randomized study with a larger sample size and a control group is necessary.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".