Baseline knee extension may be associated with volumetric cartilage loss in the anterolateral tibia: data from the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVES: Animal studies suggest regional unloading of the knee due to flexion contracture (FC) results in cartilage loss in the anterior tibia. We looked for an association between the range of knee extension and articular cartilage thickness in the tibia of patients with knee OA, using quantitative MRI data from the OA Initiative. METHODS: Baseline knee extension was measured using a goniometer. Cartilage thickness was measured using 3-Tesla coronal MRI images of the knee. The tibia articular cartilage was segmented into medial and lateral regions, then further divided into anterior, central and posterior subregions. We evaluated differences between participants with and without a knee FC and associations between knee extension and cartilage thickness, including percentage denudation of bones (0 mm thickness), using linear models. RESULTS: A total of 596 participants were included. Participants with a knee FC had a larger percentage of denuded bone in the anterolateral tibia vs participants without FC (2.2 ± 0.7% vs 0.4 ± 0.1%; P = 0.006), and knee extension was associated with anterolateral tibia denuded bone (r = 0.16, P < 0.001). After correcting for demographics, knee alignment, and OA severity, presence of FC and lost knee extension were associated with the percentage of denuded bone in the anterolateral tibia [β = 1.702 (0.634-2.770) and β = 0.261 (95% CI 0.134, 0.388), respectively]. CONCLUSION: While causation cannot be determined in this study, limitation in knee extension was statistically associated with the percentage of denuded bone in the anterolateral tibia. These novel data support that maintaining range of motion over the entire joint surface may help preserve articular cartilage health.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".