Association between knee stiffness and MRI-detected lesions: data from the osteoarthritis initiative (OAI)
Bibliographic record
Abstract
OBJECTIVE: To explore the association between knee stiffness and MRI-detected lesions. METHODS: A within-person knee-matched case-control study was performed. We included participants from the Osteoarthritis Initiative (OAI) whose stiffness score differed by ≥ 1 point between their knees, as assessed by the Western Ontario and McMasters Universities Osteoarthritis Index (WOMAC) stiffness subscales. We evaluated six different intra-knee structural lesions using the MRI Osteoarthritis Knee Score (MOAKS). A latent class analysis was used to identify distinct subgroups of MRI-detected lesions. Then, we conducted a conditional logistic regression model to explore the association between these subgroups and knee stiffness. RESULTS: Out of the 149 patients who were eligible, we categorized their knees into four subgroups based on the types of MRI-detected lesions: (I) minimal lesions, (II) mild lesions, (III) moderate lesions, and (IV) severe lesions. Compared with subgroup I, there were no significant differences in the likelihood of having greater knee stiffness at morning wake-up in subgroups II to III, while there was a significantly increased risk of greater knee stiffness in subgroup IV. Compared with subgroup I, there was no significant difference in the likelihood of having greater knee stiffness after sitting, lying, or resting in subgroup II, while there was a significantly increased risk of greater knee stiffness in subgroups III to IV. CONCLUSION: We found that meniscus lesions, synovitis, and effusion were associated with knee stiffness. However, neither cartilage lesions nor bone marrow lesions in the tibio-femoral or patella-femoral joints were significantly correlated with knee stiffness.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".