Osteoarthritis: MR Imaging Findings in Different Stages of Disease and Correlation with Clinical Findings
Bibliographic record
Abstract
PURPOSE: To determine whether knee pain, stiffness, and limited function in patients with different stages of osteoarthritis correlate with the degree of disease assessed on magnetic resonance (MR) images and radiographs. MATERIALS AND METHODS: Radiographs in 50 patients with varying degrees of osteoarthritis of the knee were assessed by using the the Western Ontario and McMaster University (WOMAC) osteoarthritis index and the Kellgren-Lawrence (KL) scale. MR images were obtained and analyzed by two readers for cartilage lesions, bone marrow edema pattern, and ligamentous and meniscal lesions. RESULTS: Thirteen of 16 knees with a KL score of 4 showed full-thickness cartilage lesions and bone marrow edema pattern. Cruciate ligament tears were found in five of 12 knees with a KL score of 3 and in nine of 16 knees with a KL score of 4. While the KL score correlated significantly (P <.05) with the grade of cartilage lesions, and a substantially higher percentage of lesions with higher KL scores were found on MR images, the correlations between MR imaging findings and KL score versus clinical findings were not significant (P >.05). Significant differences between WOMAC scores were found only for the grades of cartilage lesions (P <.05). CONCLUSION: Cartilage lesions, bone marrow edema pattern, and meniscal and ligamentous lesions were frequently demonstrated on MR images in patients with advanced osteoarthritis. Clinical findings showed no significant correlations with KL score and extent of findings at MR imaging.
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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 | 0.001 |
| 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".