Hip Shape as a Predictor of Osteoarthritis Progression in a Prospective Population Cohort
Bibliographic record
Abstract
OBJECTIVE: Hip morphology plays a significant role in the incidence and progression of hip osteoarthritis (OA). We hypothesized that hip shape would also be associated with other key factors and tested this in a longitudinal community-based cohort combining radiographic, magnetic resonance imaging (MRI), dual-energy x-ray absorptiometry (DXA), and clinical data. METHODS: Baseline DXA images of the left hip of 831 subjects from the Tasmanian Older Adult Cohort were analyzed using an 85-point statistical shape model. Hip pain was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index, and muscle strength was measured using a dynamometer. Hip structural changes were assessed using MRI and radiographic OA using plain radiographs. RESULTS: Six shape modes accounted for 68% of shape variation. At baseline, modes 1, 2, 4, and 6 were associated with radiographic hip OA; modes 1, 3, 4, and 6 were correlated with hip cartilage volume; and all except mode 2 were correlated with muscle strength. Higher mode 1 and lower mode 3 and mode 6 scores at baseline predicted hip pain at followup and higher mode 1 and mode 2 scores were associated with hip effusion-synovitis. Higher scores for mode 2 (decreasing acetabular coverage) and lower scores for mode 4 (nonspherical femoral head) at baseline predicted 10-year total hip replacement (THR), while mode 4 alone was correlated with bone marrow lesions (BMLs), effusion-synovitis, and increased cartilage signal. CONCLUSION: Hip shape is associated with radiographic OA, THR, hip pain, effusion-synovitis, BMLs, muscle strength, and hip structural changes. These data suggest that different shape modes reflect multiple facets of hip OA.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".