Auricular acupressure for treating early stage of knee osteoarthritis: a randomized, sham-controlled prospective study
Bibliographic record
Abstract
OBJECTIVE: We examined whether auricular acupressure (AA) at four specifically preselected AA points can alleviate knee pain and decrease non-steroidal anti-inflammatory drugs (NSAIDs) consumption and its adverse effects for osteoarthritis patients. METHODS: Sixty-two patients (more than 40 years) with knee osteoarthritis of Kellgren-Lawrence grades of I or II upon radiographic classification were enrolled in this randomized, sham-controlled prospective study, and divided into two groups (AA group and control group). The AA group received true AA by embedding vaccaria seeds at four specific AA points (knee joint, shenmen, subcortex and sympathesis) ipsilateral to the knee osteoarthritis site, while the control group received four nonacupuncture points on the auricular helix. Visual analog scale (VAS), the Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, the number of patients who needed celecoxib pills and adverse effects were recorded. RESULTS: VAS and WOMAC scores in the AA group were significantly lower than that in the control group (P < 0.05) at Days 3 and 7 postsugery. The VAS and WOMAC score were significantly decreased after the treatment in the AA group compared with that before the treatment (P < 0.05). The use of celebrex is significantly lower in the AA group than in the control group (P < 0.05), no major side effects were observed during the auricular acupressure treatment. CONCLUSION: Auricular acupressure plays a role in analgesic effect and can effectively decrease NSAIDs requirements without causing adverse events for the treatment of human knee osteoarthritis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".