Analysis of the precision and sensitivity to change of different approaches to assess cartilage loss by quantitative MRI in a longitudinal multicentre clinical trial in patients with knee osteoarthritis
Bibliographic record
Abstract
INTRODUCTION: Cartilage thickness and volume loss measurements using quantitative magnetic resonance imaging (qMRI) are suggested to detect significant cartilage changes over short time intervals. We aimed to compare these two different approaches looking at the global knee and subregions, using data from an osteoarthritis (OA) multicentre randomised clinical trial. METHODS: Three hundred and fifty-five patients with symptomatic knee OA were recruited for a two-year, double-blind, randomised clinical trial evaluating the effect of 200 mg licofelone twice daily and 500 mg naproxen twice daily on cartilage loss, and 301 patients had baseline MRI. MRIs were performed at baseline, 6, 12 and 24 months. Cartilage volume and thickness in the global joint, medial and lateral compartments, and central weight-bearing subregions of the medial and lateral femoral condyles and tibial plateaus were analysed. Data were analysed for the mean value imputed for intent-to-treat (ITT-MVI) and statistical analyses were performed using two-sample Student's t-test. RESULTS: Cartilage mean thickness loss in the global joint, lateral and medial compartments, as well as in medial compartments stratified according to patients with or without meniscal extrusion, was significantly less in the licofelone compared with the naproxen group at 12 and 24 months. Interestingly, these data were similar to those found when using cartilage volume loss as an outcome. Although greater cartilage volume and mean thickness loss was seen in central weight-bearing subregions of the medial and lateral compartments compared with the whole compartment and also in patients with meniscal lesions/extrusion, suggesting good sensitivity to change, its high standard deviation precluded for the condyles a high statistical power and abrogated statistically significant differences between the treatment groups. CONCLUSIONS: These data demonstrate that both the measurement of cartilage thickness and that of cartilage volume provide the same level of sensitivity to estimate cartilage loss in a clinical trial. However, the potential of gaining statistical power with the use of thickness/volume change in knee subregions as an outcome seems negated by high inter-patient variability. Moreover, there is no superiority in statistical power by selecting patients with meniscal extrusion.
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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 | 0.080 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".