Association between hip and knee cartilage measured using radiographs and magnetic resonance imaging: the Tasmanian Older Adult Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Cartilage loss is a key pathological feature of OA and can be assessed indirectly using radiography or directly through MRI. A number of cross-sectional studies have suggested that primary generalized osteoarthritis (PGOA) may be a distinct disease, but despite the high frequency of involvement of the hip and the knee joints in OA, very few studies have looked at the radiographic association between these two joints, and none has done so using MRI. The aim of this study was to examine the association of hip and knee cartilage measured by both radiography and MRI. METHODS: We studied 151 participants from the Tasmanian Older Adult Cohort (TASOAC) study, who were selected randomly from the southern Tasmanian electoral rolls. MRI was used to assess hip and knee cartilage volume and radiography was used to assess joint space narrowing (JSN). Correlation analyses were used to compare cartilage volume measurements and JSN. RESULTS: In adjusted analysis, there was a consistent, positive association between knee and hip cartilage volume that was best for total knee cartilage volume (r = 0.16-0.40, all P < 0.05). In contrast, there was at best a weak correlation, depending on the site, between hip and knee JSN (r = -0.01 to 0.21). CONCLUSION: Hip and knee cartilage volume are more strongly associated than hip and knee JSN, suggesting a commonality of cartilage volume at different anatomic sites. The weaker radiographic association may reflect less measurement error with MRI or the contribution of multiple structures to joint space in the knee.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".