THE TREATMENT WITH EXTRACORPEREAL SHOCK WAVE THERAPY IN SOME OF MOST FREQUENTLY MUSCULOSKELETAL PATHOLOGIES
Bibliographic record
Abstract
Background: The aim of this study is to evaluate the efficacy of extracorpereal shock wave therapy (ESWT) in some of most frequent muscularskeletal pathologies. Material and methods: From July to October 2004 310 patients were treated with ESWT, suffering from the following pathologies: 96 symptomatic calcific tendonitis of the shoulder, 53 symptomatic sub-acromial impingement, 48 humeral epichondylitis, 52 plantar fasciitis, 24 pertrochanteric bursitis, 15 Achilleous tendinopathy and 22 patellar tendinopathy. Patients were evaluated clinically and instrumentally before the first application and at one and three months of follow-up. Three disability scales we utilized (NRS, Mcgill Pain Questionnaire e Chronic Pain Grade Questionnaire). Results: We observed a reduction of the pain and an increase of the articular functionality in 83% of calcific tendonitis of the shoulder, in 55% of sub-acromial impingement, in 76% of epichondylitis, in 74% of palantar fasciitis, in 90% of pertrochanteric bursitis, in 82% of Achilleous tendinopathy and in 86% of patellar tendinopaty. Discussion: The data confirm the therapy with ESWT is efficient in some of most frequent musculoskeletal pathologies, with variable outcome in the various pathologies under investigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".