Evaluation of the effect of low-intensity pulsed ultrasound in pain and dysfunction for knee osteoarthritis: a double-blind, randomised controlled trial protocol
Bibliographic record
Abstract
INTRODUCTION: Osteoarthritis (OA) is the most common joint disorder among musculoskeletal conditions. Non-surgical treatment is the standard therapy for knee OA (KOA). Ultrasound therapy is recommended for alleviating pain and dysfunction from OA, but high-quality scientific evidence for its effectiveness in OA treatment is still lacking.Therefore, we want to analyse whether combining conventional physical therapy with low-intensity pulsed ultrasound (LIPUS) can enhance the efficacy of conventional therapy, thus improving symptoms in patients with KOA. METHODS AND ANALYSIS: This randomised controlled trial aims to recruit 200 patients diagnosed with KOA, aged 38 years or above, who meet the clinical diagnostic criteria for KOA. Patients will be randomly assigned in a 1:1 ratio to either a LIPUS treatment group or a sham ultrasound treatment control group. The 2-week treatment will consist of five sessions per week and evaluations will take place at baseline, on the day of the last intervention and 1 month post intervention. The main outcome measures will be the Western Ontario and McMaster Universities' scores. Secondary outcome indicators will be the Numerical Pain Rating Scale, the Lequesne scale, the time up and go test and the range of motion of the knee. An intention-to-treat analysis will be performed for dropouts and missing data. ETHICS AND DISSEMINATION: The study was approved by the ethics committee of Shengjing Hospital of China Medical University (2023PS592K). Findings will be disseminated to participants and made available to peer-reviewed journals. TRIAL REGISTRATION NUMBER: The trial was registered on the Chinese Clinical Trial Registry platform (chictr.org.cn) on 22 March 2023, with the registration ID ChiCTR2300069643.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.008 |
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".