Clinical Effects of Ultrasound-guided Acupotomy in Knee Osteoarthritis Treatment
Bibliographic record
Abstract
The protocol presented here demonstrates the operation method of ultrasound-guided acupotomy for knee osteoarthritis (KOA), including patient recruitment, preoperative preparation, manual operation, and postoperative care. The purpose of this protocol is to relieve pain and improve knee function in patients with KOA. A total of 60 patients with KOA admitted between June 2022 and June 2023 were treated with ultrasound-guided acupotomy. Pathological changes and knee function scores were compared before and after the treatment. After 1 week of treatment, the synovial thickness of the suprapatellar bursae was significantly lesser than before treatment (p < 0.05), the Hospital for Special Surgery Knee Score (HSS) was significantly higher than before treatment (p < 0.05), the Visual analogue scale (VAS) was significantly lower than those of the control group (p < 0.05) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were significantly lower than those of the control group (p < 0.05). Therefore, ultrasound-guided acupotomy for the treatment of KOA can reduce synovial thickness, relieve pain, improve knee joint function, and have a remarkable curative effect.
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 | 0.001 | 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 | 0.001 | 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".