Offset analgesia a unique paradigm to study pain modulation following 6 weeks of yogic intervention in fibromyalgia patients: A randomized controlled trial
Bibliographic record
Abstract
ABSTRACT Aim: The aim of this study was to evaluate the effect of supervised yoga therapy on pain modulation through the offset analgesia (OA) paradigm of quantitative sensory testing (QST). Materials and Method: Eighty female FM patients were recruited and randomized into yoga and standard care (SC) groups. Pre- and post-6-week assessments were done for subjective assessment of pain using VAS, widespread pain index, and McGill pain questionnaire-short form. This was followed by thermal assessment (hot pain tolerance threshold) at C8 dermatome of the left forearm to test OA at three randomized sites for 20 patients in each group. OA included experimental trial, repeated OA trial, and downward step test trial. The Normality test was done using the D’Agostino and Pearson normality test. Data found to be normally distributed was compared using paired t-test or Unpaired t-test for within-group and between-group, respectively. Data which did not follow Gaussian distribution was tested using the Wilcoxon signed-rank test or Mann–Whitney test for within-group and between-group, respectively. Results: Significance reduction in VAS scores (P<0.05) of pain status was observed after six weeks of yoga intervention. Significant reduction in pain and FM-related symptoms after 6 weeks in FM patients participating in yoga classes was observed. Objective assessment of pain was done using QST (thermal modality) for OA at baseline and 6 weeks. Significant increase in change in VAS minimum (ΔOA), Latency, and OA index was observed after yoga in all the OA paradigms. Conclusions: Yoga is an effective complementary therapy for reducing pain and improving quality of life in FM patients after 6 weeks. Additional randomized controlled trials with larger sample sizes are needed to more fully understand this relationship.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".