Objective evidence for chronic back pain relief by Medical Yoga therapy
Bibliographic record
Abstract
Chronic low back pain (CLBP) is a musculoskeletal ailment that affects millions globally. The pain is disturbing associated with impaired motor activity, reduced flexibility, decreased productivity and strained interpersonal relationships leading to poor quality of life. Inflammatory mediators in vicinity of nociceptors and amplification of neural signals cause peripheral and central sensitization presented as hyperalgesia and/or allodynia. It could be attributed to either diminished descending pain inhibition or exaggerated ascending pain facilitation. Objective measurement of pain is crucial for diagnosis and management. Nociceptive flexion reflex is a reliable and objective tool for measurement of a subject's pain experience. Medical Yoga Therapy (MYT) has proven to relieve chronic pain, but objective evidence-based assessment of its effects is still lacking. We objectively assessed effect of MYT on pain and quality of life in CLBP patients. We recorded VAS (Visual analogue scale), McGill Pain questionnaire and WHOQOL BREF questionnaire scores, NFR response and Diffuse noxious inhibitory control tests. Medical yoga therapy consisted of an 8-week program (4 weeks supervised and 4 weeks at home practice). CLBP patients (42.5 ± 12.6 years) were randomly allocated to MYT (n = 58) and SCT groups (n = 50), and comparisons between the groups and within the groups were done at baseline and at end of 4 and 8 weeks of both interventions. (VAS) scores for patients in both the groups were comparable at baseline, subjective pain rating decreased significantly more after MYT compared to SCT (p = < 0.0001*, p = 0.005*). McGill Pain questionnaire scores revealed significant reduction in pain experience in MYT group compared to SCT. Nociceptive Flexion Reflex threshold increased significantly in MYT group at end of 4 weeks and 8 weeks, p < 0.0001#, p = < 0.0001∞ respectively) whereas for SCT we did not find any significant change in NFR thresholds. DNIC assessed by CPT also showed significant improvement in descending pain modulation after MYT compared to SCT both at end of 4 and 8 weeks. Quality of life also improved significantly more after MYT. Thus, we conclude with objective evidence that Medical Yoga Therapy relieves chronic low back pain, stress and improves quality of life better than standard care.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".