Low-Energy Versus Middle-Energy Extracorporeal Shockwave Therapy for Treating Pes Anserine Bursitis
Bibliographic record
Abstract
Introduction: Pes anserine bursitis (PAB) is a painful status inside the knee that may interfere with functional activities. Extracorporeal shockwave therapy (ESWƬ) may treat this disorder. Objective: Comparing the effects of low- versus middle-energy ESWƬ on pain and functional activity in patients with sub-acute PAB. Materials and Methods The study was a single-blind randomized trial. Twenty-eight patients with sub-acute PAB were randomly divided into two groups and received either low or middleenergy ESWƬ for three weeks. The numeric pain rating scale (NPRS), short-form McGill pain questionnaire (SF-MPQ), timed up and go (TUG) test, and Western Ontario and McMaster universities index (WOMAC) were evaluated before and 2 and 3 weeks after the intervention. Results: A significant improvement was observed for low-energy ESWT in terms of NPRS (P=0.001), SF-MPQ (P<0.001), WOMAC (P<0.001), and TUG (P<0.001) 3 weeks after the intervention. Also, a significant improvement was observed following middle-energy ESWT application on NPRS (P=0.003), SF-MPQ (P<0.001), WOMAC (P<0.001), and TUG (P<0.001) 3 weeks after the intervention. A similar trend was observed between study time points and for all variables in each group. The only exception was the TUG, which showed no improvement between 2 and 3 weeks after the intervention for each study group. A significant improvement was observed in the NPRS between the two groups after 2 weeks (P=0.001) and 3 weeks (P=0.006), both favoring the middle-energy ESWT application. Conclusion: Low- and middle-energy ESWT can effectively improve pain, functional activity, and mobility in patients with PAB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".