Relation of <scp>MRI‐Detected</scp> Features of Patellofemoral Osteoarthritis to Pain, <scp>Performance‐Based</scp> Function, and Daily Walking: The Multicenter Osteoarthritis Study
Bibliographic record
Abstract
OBJECTIVE: The study objective was to determine the relationship of magnetic resonance imaging (MRI)-detected features of patellofemoral joint osteoarthritis to pain and functional outcomes. METHODS: We sampled 1,099 participants from the 60-month visit of the Multicenter Osteoarthritis Study (mean ± SD age: 66.8 ± 7.5 years; body mass index: 29.6 ± 4.8; 65% female). We determined the prevalence of MRI-detected features of patellofemoral joint osteoarthritis (eg, cartilage damage, bone marrow lesions, and osteophytes) and assessed the relationship between these features and knee pain severity, knee pain on stairs, chair stand time, and walking less than 6,000 steps per day. We evaluated the relationship of MRI features to each outcome using logistic and linear regression, adjusting for potential covariates. RESULTS: Participants with cartilage damage in 3-4 subregions had the highest mean pain severity (22.0/100; 95% confidence interval [CI]: 17.6-26.4 mm). They also showed higher odds of having at least mild pain on stairs (odds ratio [OR]: 3.3; 95% CI: 1.7-6.5) and of walking less than 6,000 steps per day (OR: 2.3; 95% CI: 1.1-4.4) compared with those without cartilage damage. Participants with bone marrow lesions in 3-4 subregions had higher odds of at least mild pain on stairs than those without (OR: 3.3; 95% CI: 2.2-5.2). Participants with osteophytes in 3-4 subregions also had higher odds of walking less than 6,000 steps/day (OR 2.1, 95% CI: 1.3-3.5, respectively). CONCLUSION: MRI-detected features of osteoarthritis of the patellofemoral joint are related to pain and functional performance. This knowledge highlights the need to develop treatments for those with patellofemoral joint osteoarthritis to improve pain and maximize function.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".