Ultrasound detection of knee joint degeneration in patients with multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: Early degeneration of the knees might occur in patients with multiple sclerosis secondary to balance and walking impairment and muscle weakness. The aims of this study were to evaluate the knee joints of patients with multiple sclerosis compared with healthy controls, using ultrasono-graphy, and to investigate whether there is any correlation between femoral cartilage degeneration and disease-related parameters. DESIGN: Study participants were 79 patients with multiple sclerosis and 60 healthy controls. The disease-related parameters, Expanded Disability Status Scale (EDSS), Western Ontario and McMaster Universities (WOMAC) osteoarthritis index, visual analogue scale (VAS) for pain severity, and Berg Balance Scale (BBS) scores were recorded. Femoral cartilage and knee effusion were evaluated using ultrasonography. RESULTS: Femoral cartilages of patients with multiple sclerosis were more degenerated than those of healthy controls. Moreover, patients with multiple sclerosis had more effusion in their knees than did controls. In the multiple sclerosis group there was no correlation between cartilage degeneration grade, amount of effusion, and VAS-pain, BBS, WOMAC and EDSS scores. CONCLUSION: Patients with multiple sclerosis may have more rapid degeneration of the knee cartilage and increased effusion compared with healthy controls. Ultrasonography is an effective method to detect these changes. However, cartilage degeneration was not found to be associated with disease-related parameters in multiple sclerosis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".