Does cartilage volume measurement or radiographic osteoarthritis at baseline independently predict ten-year cartilage volume loss?
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to examine whether cartilage volume as measured by MRI and radiographic osteoarthritis (OA) at baseline predict cartilage volume loss over ten years independent of each other and other structural co-pathologies. METHODS: 219 participants [mean-age 45(26-61); 57 % female] were studied at baseline and ten years. Approximately half were the adult offspring of subjects who underwent knee replacement for OA and the remainder were randomly selected controls. Joint space narrowing (JSN) and osteophytes were assessed on radiographs and cartilage volume (tibiofemoral), cartilage defects, bone marrow lesions and meniscal tears/extrusion were assessed on MRI. RESULTS: Mean absolute and percentage per annum cartilage volume loss was 1284 mm(3) and 1.91 % respectively in the medial compartment and 1007 mm(3) and 1.38 % respectively in the lateral compartment. Higher baseline tibiofemoral cartilage volume was independently associated with greater absolute cartilage volume loss in both medial (β(95 % CI) = -300 (-399,-200)) and lateral (β = -338 (-443,-233)) compartments and percentage per annum loss in the lateral compartment(β = -0.15 (-0.29, -0.01)). Baseline JSN and osteophytes were associated with cartilage volume loss in the univariable analysis, however these associations did not persist after adjustment for other structural co-pathologies. CONCLUSION: Cross-sectional cartilage volume measurement independently predicts cartilage volume loss over 10 years and can be used to identify fast progressors in clinical trials. Radiographic JSN and osteophytes on the other hand are a reflection of other co-pathologies assessed on MRI and do not independently predict cartilage volume loss over 10 years.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".