Effect of functional knee loading on articular cartilage MRI T2 relaxation time and thickness in patients at risk for knee osteoarthritis
Bibliographic record
Abstract
Objectives: The objectives of this study were: 1) to evaluate the effect of a functional loading stimulus on MRI-acquired T2 relaxation time (T2) and thickness of knee articular cartilage, and 2) to compare the response between patients at risk for knee OA and healthy controls. Design: A total of 32 participants (16 healthy controls [24.7 ± 3.0 years], and 16 at-risk participants [37.5 ± 12.2]) underwent 3T MRI T2 mapping scans immediately before and after a standardized 25-minute functional loading stimulus on a computerized treadmill that included a variety of challenging walking conditions. Groups were defined using the Osteoarthritis Initiative Control (healthy) and Incidence Cohort (at-risk) Criteria. We analyzed changes in T2 between groups in the superficial and deep layers of tibiofemoral, patellar, and trochlear cartilage, and for tibiofemoral cartilage thickness using multivariate linear mixed-effects models. Results: T2 was shorter in the superficial cartilage layers in both groups. The mean combined change (95 % confidence interval) in T2 of the superficial layer was -3.80 ms (-4.87; -2.73) for at-risk participants and -3.89 ms (-4.96; -2.82) for healthy controls. The between-group difference in change was 0.09 ms (-1.04; 1.22). There was a decrease in articular cartilage thickness in the lateral compartment for healthy controls (-0.14 mm [-0.24; -0.04]), otherwise there were no changes detected. Conclusions: Consistently shorter T2 was observed in the articular cartilage of patients at risk for knee OA and in healthy controls, after a challenging walking test, but with no concurrent change in cartilage thickness, suggesting a similar articular cartilage response to functional loading.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".