Knee Alignment Is Quantitatively Related to Periarticular Bone Morphometry and Density, Especially in Patients With Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Static alignment influences knee loading and predicts osteoarthritis (OA) progression. Periarticular bone is important in dispersing forces across the knee, and there is substantial evidence for molecular crosstalk between cartilage and subchondral bone. The aim of this study was to evaluate the relationship between periarticular trabecular bone morphology and bone mineral density (BMD) and knee alignment in OA. METHODS: This was a cross-sectional analysis of participants in the Osteoarthritis Initiative Bone Ancillary Study. Dual x-ray absorptiometry (DXA) was performed to measure tibial periarticular bone mineral density (paBMD). Magnetic resonance imaging of knee trabecular bone was performed to calculate the apparent bone volume fraction (aBVF), apparent trabecular number (aTbN), apparent trabecular spacing (aTbSp), and apparent trabecular thickness (aTbTh). Static alignment was assessed by measuring the hip-knee-ankle (HKA) angle on long-limb films. RESULTS: ), 71% of whom had OA. Correlations between the HKA angle and medial:lateral paBMD, medial paBMD, aBVF, aTbN, aTbTh, and aTbSp were -0.63, -0.34, -0.29, -0.32, -0.22, and 0.30, respectively. More varus alignment was associated with higher medial:lateral paBMD, medial paBMD, aBVF, aTbN, aTbTh, and lower aTbSp. In OA knees, the results were more pronounced. In non-OA knees, the most consistent association was with medial:lateral paBMD. CONCLUSION: Static alignment was associated with medial:lateral paBMD in all knees and with medial paBMD and trabecular morphometry in OA knees only. Aberrant knee loading may lead to increased relative subchondral bone density, which is partly related to a higher aBVF and a greater number of thicker trabeculae with smaller intertrabecular spacing. Knee DXA may be a useful early biomarker of knee OA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".