Quantitative magnetic resonance imaging of articular cartilage in knee osteoarthritis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Attempts to evaluate knee cartilage damage and progression seem logical in osteoarthritis research. Magnetic resonance imaging allows for precise visualization of joint structures such as cartilage, bone, synovial tissues, ligaments and menisci, and their pathologic changes. RECENT FINDINGS: Recent advances in magnetic resonance technology have enabled researchers to evaluate cartilage damage and progression over the cross-sectional and longitudinal planes. Although anatomic changes can be seen, for many years the quantification of the cartilage changes has been the real challenge. Quantitative assessment of cartilage morphology using magnetic resonance imaging with fat-suppressed gradient echo sequences and digital postprocessing techniques provides high accuracy and adequate precision for cross-sectional and longitudinal studies in osteoarthritis patients. Recent data on precision, reliability, and sensitivity to change of quantitative parameters of cartilage morphology in osteoarthritis are presented in this review. Longitudinal studies currently available suggest that changes of cartilage volume, potentially as much as 5% per year, occur in osteoarthritis in most knee compartments, exceeding the variability of these measurements. SUMMARY: Magnetic resonance imaging provides reliable and quantitative data on cartilage status throughout all compartments of the knee, and robust acquisition protocols for multicenter trials are now available. Magnetic resonance imaging technology should hopefully reduce the number of patients needed in clinical trials, improve retention of these patients, and reduce the overall costs and the length of clinical trials of treatment response to disease-modifying osteoarthritis drugs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".