A comparison of different physical stimulation combined with platelet-rich plasma for the treatment of knee osteoarthritis: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Platelet-rich plasma (PRP) contains various growth factors and inflammatory regulators, which can effectively reduce inflammation in joints and promote tissue repair. Multiple studies have proved its effectiveness in the treatment of knee osteoarthritis (KOA). Low-intensity focused ultrasound (LIFU) and transcutaneous electrical nerve stimulation (TENS) are non-invasive and safe physical therapy methods for KOA. This study is the first to propose the treatment of KOA with physical stimulation after PRP treatment, and to observe the clinical efficacy of the treatment method. METHODS: This is a protocol paper that outlines a randomized controlled trial, patients will be assigned randomly to the PRP group, PRP+LIFU group, PRP+TENS group, and PRP+LIFU combined TENS group. The patients will be followed at 12-week and 24-week time points to evaluate the primary and secondary outcomes of the study. The primary outcome is the VAS pain score. Secondary outcomes include Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and International Knee Documentation Committee scores (IKDC). After baseline examination, all patients will sign a written informed consent for study participation after a full explanation of the treatment protocol. We have planned a total of 120 patients (30 patients per group). DISCUSSION: The objective of this clinical trial is to evaluate the effect of different physical stimulation after PRP treatment for KOA. The data will be published after the completion of the study. TRIAL REGISTRATION: This study has been registered with the Chinese Clinical Trials Registry. REGISTRATION NUMBER: ChiCTR2200065119 (registered date: 10/28/2022).
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".