The efficacy of low-intensity pulsed ultrasound on articular cartilage and clinical evaluations in patients with knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: While a number of preclinical studies have examined the effectiveness of low-intensity pulsed ultrasound (LIPUS) as a potential treatment for knee osteoarthritis (OA), there have been few clinical studies which have indirectly confirmed cartilage regeneration by magnetic resonance imaging (MRI). OBJECTIVE: The aim of this clinical trial was to investigate whether LIPUS effectively increased knee cartilage thickness and improved pain and function in knee OA patients. METHODS: This study was a prospective, single-group, home-based self-therapy trial. We included patients (n= 20) with OA pain. Each patient used an ultrasonic stimulation device (BODITREK JOINT™) for more than 20 sessions. Outcomes were assessed by MRI, Visual Analogue Scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the 36-Item Short Form Survey (SF-36) for assessing quality of life. RESULTS: Nineteen subjects completed this study. There was no significant increase in the cartilage thickness measured by MRI after LIPUS treatment. LIPUS therapy significantly decreased VAS score and WOMAC score, and significantly increased SF-36 score. The subgroup analysis in patients with knee OA showed that LIPUS treatment showed better for older patients with lower Kellgren-Lawrence grades. CONCLUSION: Pain, function, and quality of life improved after LIPUS, but there was no significant increase in cartilage thickness through MRI.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".