Acupotomy for Osteoarthritis of the Knee; A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The purpose of this study was to evaluate the effectiveness and safety of acupotomy for the treatment of patients with knee osteoarthritis. There were 9 databases searched to retrieve randomized controlled trials until August 3, 2019 regarding acupotomy versus conventional Western medicine, conventional Western medicine treatment with and without acupotomy, and Korean medicine treatment with and without acupotomy, and meta-analysis was performed. Of 303 potentially relevant studies retrieved, 43 were systematically reviewed. All studies were conducted in China. Effective rate, visual analogue scale, and Western Ontario and McMaster Universities Osteoarthritis index were used as the evaluation scales. The Ashi point was selected most frequently. In all studies, the intervention group was more effective than the control group. Meta-analysis revealed that acupotomy showed statistically significant beneficial results. Although acupotomy had a beneficial effect on knee osteoarthritis, the risk of bias of the included studies was not low. The majority of the results from the evaluation scales used were highly heterogeneous (> 50%) which reduced confidence in the estimation of effect, or had a small sample size. Further clinical research and development is required in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.020 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".