Relationship Between Pain Severity and Magnetic Resonance Imaging Features in Patients with Osteoarthritis of The Knee
Bibliographic record
Abstract
Objective: To evaluate the association between clinical symptoms and magnetic resonance imaging (MRI) findings in patients with osteoarthritis (OA) of the knee. Materials and Methods: Ten men and 24 women between 30 and 60 years of age, who fulfilled the American College of Rheumatology (ACR) criteria for knee OA, were included in the study. All patients underwent MRI of the more symptomatic knee and the MRI findings were evaluated by the same radiologist blinded to clinical findings, using a semi-quantitative whole-organ MRI scoring method (WORMS). The Western Ontario and Mc-Master University (WOMAC) osteoarthritis index was used to assess physical function, morning stiffness, and joint pain. Results: Linear regression analysis revealed that the total WORMS score and effusion severity were the most important predictors of the WOMAC pain score. The volume of the effusion was significantly correlated with the WOMAC pain and disability scores (r=0.601, p
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".