Acupuncture Plus Cupping Relieve Pain and Reduce the Consumption of Analgesics in Patients with Advanced Knee Osteoarthritis
Bibliographic record
Abstract
Abstract: Acupuncture is a nonpharmacological option described in the literature; however, its efficacy in the treatment of knee osteoarthritis (KOA) is still controversial. This randomized, sham-controlled trial evaluated before-and-after differences in pain relief visual analog scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index Pain Section (WOMAC-Pain) among advanced patients with KOA assigned to acupuncture, cupping, combined therapy, or sham controls. Abstract: A total of 120 patients waiting for total knee arthroplasty from the knee outpatient unit of the Institute of Orthopedics and Traumatology, "Hospital das Clínicas" of the University of São Paulo Medical School, were invited to participate in the project. Inclusion and exclusion criteria were applied, and those who accepted signed an informed consent form and were later randomly allocated (https://www.random.org) to the following groups: Group A-Sham Acupuncture + Sham Cupping; Group B-Sham Acupuncture + True Cupping; Group C-True Acupuncture + Sham Cupping; Group D-True Acupuncture + True Cupping. The VAS and WOMAC-Pain questionnaire were used to measure the intensity of pain, and the number of analgesics and/or anti-inflammatory drugs used during the research period was also recorded. Abstract: = 0.001). No differences were found in WOMAC-Pain index (0.258). Abstract: The combination of acupuncture with cupping therapy demonstrated pain improvement by VAS and decreased the amount of pain medication used by patients, suggesting a complementary yet significant role in preoperative management.
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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 | 0.001 |
| 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) | 0.004 | 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".