Development of generic yoga therapy protocol for nonspecific chronic low back pain
Bibliographic record
Abstract
A BSTRACT Objective: To develop a generic yoga therapy protocol for nonspecific chronic low back pain (NCLBP) on the basis of previous research studies. Methods: A comprehensive PubMed search was done using keywords of “yoga” and “back pain” for English-language articles published till November 2022. PubMed showed 332 results for the keywords from 1977 to 2022. Twenty-nine studies were found eligible and the consolidation of yogic techniques were used in the studies. The study compiled 72 yogic techniques, including Asana and Pranayama, from 332 articles on yoga and back pain. Out of these, 151 were not relevant to yoga or back pain. After analyzing 181 articles, 33 were selected for review for a yoga therapy protocol, with four excluded due to inability to access full-text articles. The protocol was later validated by 14 eminent yoga therapy experts across the globe from countries including Australia, France, USA, Canada, Italy, Switzerland, and India, who had a minimum of 10 years’ experience in the field. The validation of the techniques was done based on a questionnaire that required classification under three categories: (1) not necessary, (2) useful but not essential, and (3) essential. All the three categories were then segregated in decreasing order of “Essential percentage” and “Weightage percentage” and the final list developed. The cutoff was that the yogic techniques must have ≥50% of acceptance by all experts. The practices were then sequenced in order of performance and cross referenced with traditional teachings. Results: The Generic Yoga Therapy Protocol for NCLBP that has been developed through this rational and logical mechanism has 18 yogic techniques selected on the basis of weightage and essential percentage. It includes 13 standing, sitting, prone, and supine postures (Asanas), four energy modulating breathing practices (Pranayamas), and one relaxation. The Shavasan relaxation received 100% approval by all experts. Conclusion: This generic yoga therapy protocol for NCLBP was developed through a comprehensive methodology that took into account the techniques used in previous research studies and was consolidated after a method of scientific validation by 14 eminent yoga therapy experts.
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.
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 | 0.048 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".