Clinical Effectiveness of CAM methods for pain reduction in acute non-specific lower back pain
Bibliographic record
Abstract
Abstract Background: Acute Nonspecific Lower Back Pain (ANLBP) significantly impacts quality of life and often leads to the use of various treatments, including Complementary and Alternative Medicine (CAM). This retrospective study examines the effectiveness of three CAM treatments: acupuncture, cupping, and sotai therapy—in managing ANLBP. Methods: The study analyzed medical records from the Doctorkits - Integrative Holistic Health Clinic in Toronto, Canada, spanning August 2018 to February 2020. Patients were retrospectively grouped into four categories: acupuncture, cupping, sotai therapy, and a control group receiving no treatment. Pain intensity was assessed using the Faces Pain Scale (FPS), with measurements recorded before the intervention and 24 hours post-intervention. Results: The study found significant reductions in Faces Pain Scale (FPS) scores across all CAM treatment groups 24 hours post-intervention compared to the control group. Specifically, the acupuncture group showed a reduction of 3.05 points, the cupping group by 3.25 points, and the sotai group by 2.95 points in FPS scores, with p-values of 9.94E-05, 3.46E-05, and 7.04E-06, respectively. These results were statistically significant, confirming the effectiveness of each CAM treatment in reducing pain intensity. Conclusion: This study provides empirical evidence supporting the clinical effectiveness of acupuncture, cupping, and sotai therapy in reducing pain levels among patients with ANLBP. These findings suggest that CAM therapies can be effective immediate options for pain alleviation in ANLBP management, warranting further investigation into their long-term effects and potential integration into standard care practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".