Effects of low-intensity pulsed ultrasound on the infrapatellar fat pad in knee osteoarthritis: a randomized, double blind, placebo-controlled trial
Bibliographic record
Abstract
[Purpose] We investigated the effects of low-intensity pulsed ultrasound (LIPUS) irradiation of the infrapatellar fat pad (IFP) combined with therapeutic exercise for management of knee osteoarthritis (knee OA). [Participants and Methods] The study included 26 patients with knee OA, who were randomized into the LIPUS group (patients underwent LIPUS + therapeutic exercise) and the therapeutic exercise group (patients underwent sham LIPUS + therapeutic exercise). We measured changes in the patellar tendon-tibial angle (PTTA) and in IFP thickness, IFP gliding, and IFP echo intensity after 10 treatment sessions to determine the effects of the aforementioned interventions. We additionally recorded changes in the visual analog scale, Timed Up and Go Test, the Western Ontario and McMaster Universities Osteoarthritis Index, and Kujala scores, as well as range of motion in each group at the same end-point. [Results] Compared with patients in the therapeutic exercise group, those in the LIPUS group showed significant post-treatment improvements in PTTA, VAS, and Kujala scores, as well as in range of motion. [Conclusion] The combined use of LIPUS irradiation of the IFP and therapeutic exercise is a safe and effective modality to reduce IFP swelling, relieve pain, and improve function in patients with 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".