Responsiveness of Magnetic Resonance Imaging-derived Measures Over 2.7 Years
Bibliographic record
Abstract
OBJECTIVE: To compare the responsiveness of magnetic resonance imaging (MRI)-derived measures of knee osteoarthritis over 2.7 years. METHODS: There were 430 community-based participants (mean age 63.0 yrs, range 51-79 yrs; 51% female) measured at baseline and 2.7 years later. MRI of the right knee at both timepoints was performed to assess cartilage volume, cartilage defects, bone marrow lesions (BML), meniscal pathology, and tibial bone area. Global measurements were calculated as the sum of tibial and femoral measures. Standardized response mean (SRM) was calculated as the mean of change divided by the SD of change. RESULTS: Global tibiofemoral cartilage volume and cartilage defects had the best SRM of -0.80 and 0.62, respectively. Site-specific measurements were lower (SRM range for cartilage volume -0.48 to -0.54 and cartilage defects 0.33 to 0.49). The SRM for BML was 0.12, meniscal pathology 0.39, and tibial bone area -0.09. Cartilage volume and/or defects tended to be more responsive in those with knee pain, those who were obese, those who were older, and those with radiographic osteoarthritis. CONCLUSION: Global cartilage volume demonstrated the best sensitivity to change, suggesting that if we relied solely on SRM to optimize clinical trial design, then cartilage volume would be the best outcome measure. However, clinical trials have shown that cartilage volume may be less responsive to treatment compared to other measures that have lower SRM (such as BML). Therefore, although one can optimize trial efficiency by finding more responsive endpoints, both sensitivity to change and magnitude of benefit should be considered.
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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 | 0.013 | 0.033 |
| 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 | 0.001 |
| 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".