The Effect and Mechanism of Botulinum Toxin Type A For Knee Osteoarthritis Through Ultrasound Guidance
Bibliographic record
Abstract
Objective: Knee osteoarthritis (OA) is a chronic and progressive disease that affects the geriatric population. OA is characterized by cartilaginous degeneration, subcartilaginous bone reconstruction and osteophyte formation. It causes joint pain, swelling, joint dysfunction and affects the quality of life, even leading to depression. The treatment of knee osteoarthritis usually includes medications, physical therapy and traditional Chinese acupuncture. These treatments could be useful for most of OA. Refractory OA in which conventional treatment is ineffective could induce intensive pain, disability and reduce the life quality of the patient. Given that, we need obtain new methods with good curative effect for refractory OA.\nBoNT-A is the marketing name given to a neurotoxin and is found to be effective for partial muscle spasm of post-stroke. Recently the use of BoNT-A is extended to be used as pain management in conditions such as low back pain and myofascial pain. Usually, injection of BoNT-A is guided through an anatomical landmark or pain location. However, there is risk for injection without ultrasound-guidance such as fat pad disturbance. So, we plan to proceed the Intra-articular injection of BoNT-A through the ultrasound-guided method for refractory knee osteoarthritis of older individuals and study changes of the knee joint before and after intervention via MRI and radiograph imaging, and provide the new choice for refractory knee osteoarthritis of older individuals.\nMethods: Sixty patients with refractory knee osteoarthritis were randomly divided into three groups (A:saline, B: BoNT-A, C: sodium hyaluronate). Evaluation of WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) questionnaire score, VAS ((Visual Analogue Scale) score and SF-36 ((the MOS item short from health survey) at baseline, 4 weeks and 8 weeks follow-up were recorded respectively.\nResults: WOMAC, VAS and SF-36 were improved in group B and group C patients compared baseline to 4 weeks and 8 weeks respectively (P < 0.05). Significant differences in improvement for WOMAC and VAS in group B vs group A and group B vs group C at 4 weeks and 8 weeks (P < 0.05). However, no changes in WOMAC, VAS and SF-36 were observed for the group A.\nConclusions: The treatments of Botulinum toxin type A were beneficial and safe for patients suffering from refractory knee OA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".