Risk factors associated with the loss of cartilage volume on weight-bearing areas in knee osteoarthritis patients assessed by quantitative magnetic resonance imaging: a longitudinal study
Bibliographic record
Abstract
The objective of this study was to identify, on a symptomatic knee osteoarthritis (OA) cohort, the risk factors associated with the progression of the disease. More specifically, we investigated the correlation between knee cartilage volume loss from subregions over the span of 24 months by means of quantitative magnetic resonance imaging (qMRI) with demographic, clinical, radiological, and MRI structural changes. A cohort of 107 patients with knee OA selected from a large trial evaluating the effect of a bisphosphonate underwent x-rays and MRI of the knee at baseline and 24 months. Joint space width (JSW) and joint space narrowing (JSN) and cartilage volume loss over time in subregions of the tibial plateaus and femoral condyles were quantitated. Structural changes in the subchondral bone (hypersignal) and in the menisci (tear and extrusion) were also evaluated. The greatest cartilage volume loss was found in the medial compartment, and risk factors included female gender, JSW, meniscal lesions, and bone changes at baseline. Subregion analysis revealed that the greatest cartilage volume loss at 24 months was found in the central area of the medial tibial plateau (15%; p < 0.0001) and of the medial femoral condyle (12%; p < 0.0001). These findings were associated with the presence at baseline of meniscal extrusion, particularly severe meniscal extrusion, medial and severe meniscal tear, bone hypersignal, high body mass index (BMI), smaller JSW, increases in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and patient global scores over time, and greater JSN. Parameters predicting medial central femoral condyle cartilage volume loss at 24 months were lateral meniscal tear, SF-36 and BMI at baseline, and JSN. At the medial central tibial plateau, the parameters were severe meniscal extrusion, severe lateral meniscal tear, and bone hypersignal in the lateral compartment at baseline, and WOMAC pain change. Meniscal damage and bone changes are the features most closely associated with the greatest subregional cartilage volume loss. Interestingly, for the first time, JSN was strongly associated with cartilage loss in the central areas of plateaus and condyles. This study also further confirms the correlation between cartilage volume loss and JSN and symptomatic changes at 24 months.
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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 | 0.000 |
| 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".