Comparison of MRI Osteoarthritis Knee Score with Clinico-Radiological Grading
Bibliographic record
Abstract
Abstract Background Knee joint osteoarthritis (OA) is among the most prevalent degenerative diseases of the joints in the body. Various scoring system exists for grading OA, such as (1) magnetic resonance imaging (MRI) Osteoarthritis Knee Score (MOAKS), (2) clinical grading by Western Ontario and McMaster Universities Arthritis Index (WOMAC), and (3) X-ray grading of the Kellgren–Lawrence grading system (K-L). Objectives To study MRI findings and MOAKS scoring of knee OA and correlation with WOMAC and K-L scoring. Setting and Design Cross-sectional study in hospital population. Materials and Methods A total 40 knee OA cases underwent an MRI of the knee. MOAKS scoring was done and compared with K-L grading and WOMAC scores. Statistical Analysis Collected data were compiled systematically and interpreted using IBM SPSS statistics software 25.0. A p-value of less than 0.05 was considered significant. Results The mean total WOMAC score was 9. K-L grade 2 was the most prevalent X-ray grade. Bone marrow lesion (BML) and cartilage loss in MOAKS score were greater in the medial femorotibial region. A moderate positive correlation was noted between the WOMAC score and K-L grade; full-thickness articular cartilage loss score at the medial femorotibial joint (MFTJ) and WOMAC score; partial-thickness articular cartilage loss score at lateral femorotibial joint (LFTJ) and WOMAC total pain score. No correlation was found between BML and pain severity score. Conclusion Higher WOMAC scores were associated with higher grades of K-L scoring and score of cartilage loss (partial and full thickness) of the MOAKS scoring system. The rest of the features of the MOAKS score (BML score, osteophyte, and synovitis) had no significant association with pain severity and K-L grading.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".