Clinical and endocrinological changes after electro-acupuncture treatment in patients with osteoarthritis of the knee
Bibliographic record
Abstract
Neurobiological mechanisms invoking the release of endogenous opioids and depression of stress hormone release are believed to be the basis of acupuncture analgesia. This study compared plasma beta-endorphin and cortisol levels with self assessment scores of intensity of pain, before and after 10 days of electro-acupuncture treatment in patients suffering from chronic pain as a result of osteoarthritis knees. Forty patients of either sex over 40 years with primary osteoarthritis knee were recruited into a single-blinded, sham-controlled study. For electro-acupuncture group the points were selected according to the Traditional Chinese Medicine Meridian Theory. In the sham group needles were inserted at random points away from true acupoints and no current was passed. Both groups were treated for 10 days with one session every day lasting for 20-25min. Pre- and post-treatment Western Ontario and McMaster Universities (WOMAC) index of osteoarthritis knee and Visual Analogue Scale (VAS) for pain were recorded and blood samples were taken for the measurement of plasma cortisol and beta-endorphin levels. Following electro-acupuncture treatment there was a significant improvement in WOMAC index and VAS (p=0.001), a significant rise in plasma beta-endorphin (p=0.001), and a significant fall in plasma cortisol (p=0.016). In conclusion electro-acupuncture resulted in an improvement in pain, stiffness and disability. Of clinical importance is that an improvement in objective measures of pain and stress/pain associated biomarkers was shown above that of a sham treatment; hence demonstrating acupuncture associated physiological changes beyond that of the placebo effects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".