Extracorporeal shock wave treatment can normalize painful bone marrow edema in knee osteoarthritis
Bibliographic record
Abstract
Bone marrow edema (BME) represents a reversible but highly painful finding in magnetic resonance imaging (MRI) of patients with knee osteoarthritis. The aim of this retrospective study was to evaluate the efficacy of extracorporeal shock wave treatment (ESWT) on painful BME in osteoarthritis of the knee.This study focuses on people who had early-to-mid stage osteoarthritis with knee pain and MRI findings of BME. Patients who underwent ESWT treatment or prescribed alendronate treatment in our department were analyzed. Knee pain and function were measured using the visual analog scale (VAS) for pain and the Western Ontario and McMaster University Osteoarthritis Index (WOMAC), respectively. The degree of BME was measured with MRI scans.A total of 126 patients who received ESWT treatment (Group A, n = 82) or alendronate treatment (Group B, n = 44) were included. All patients were followed up clinically and radiographically for a minimum of 12 months. The mean follow-up was 23.5 months (range, 12-38 months). The VAS and WOMAC score decreased more significantly after treatment in Group A than that in Group B (P <.01) within 3 months. In 6-month MRI follow-ups, there was higher incidence of distinct reduction and complete regression of BME of the affected knee in Group A than that in Group B (P <.01).ESWT is an effective, reliable, and noninvasive treatment in patients with painful BME in osteoarthritis of the knee followed by a rapid normalization of the MRI appearance. It has the potential to shorten the natural course of this disease.
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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".