The Impact of Different Physiotherapy Programs on the Treatment of Low Back Pain
Bibliographic record
Abstract
Low back pain is one of the biggest problems in modern world. Different physiotherapy programs are used for its treatment. Lumbar traction, has been used since the time of Hippocrates, but it is still the object of discussion. The purpose of this study was to determine the impact of traditional physiotherapy (control group), and traction therapy with TherapiMaster equipment (research group) on treatment of low back pain. Subjects: 40 patients from 34 to 62 years of age with low back pain participated in the study. Methods: static and dynamic trunk muscle endurance, low back pain intensity (according to Numeric Rating Scale, and McGill pain questionnaire), and functional state (according to Roland-Morris questionnaire, and Oswestry disability index) of patients were evaluated. Patients of control group received 14 procedures of traditional physiotherapy. The patients in the research group received 14 procedures of lumbar traction with TerapiMaster equipment and exercises. Conclusions: Physiotherapy procedures improved static and dynamic trunk muscle endurance in both control and research groups (p < 0.01), reduced low back pain (p < 0.01), and improved the patients’ functional state in both groups (p < 0.01). The physiotherapy program with lumbar traction had better effect only relieving back pain assessed by McGill pain questionnaire compared to traditional physiotherapy (p < 0.05).Keywords: low back pain, lumbar traction, physiotherapy, Roland-Morris and McGill questionnaires, Oswestry disability index.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".