The Association of Cartilage Volume with Knee Pain in Osteoarthritis: An Atlas-Based vs Native Approach
Bibliographic record
Abstract
Motivation: This research investigates the limited association between cartilage volume and knee pain in osteoarthritis, potentially confounded by anatomical variability. Goal(s): To enhance the correlation between MRI-derived cartilage volumes and Western Ontario and McMaster Universities Osteoarthritis (WOMAC) pain scores using a knee atlas for image registration. Approach: Using data from the Osteoarthritis Initiative, MRIs of subjects with OA were registered to an anatomical template. The atlas-based measurements were compared with traditional methods to assess the impact on correlation with WOMAC pain scores. Results: Atlas registration resulted in more consistent cartilage volume measures, reducing variability, and doubling the correlation with WOMAC pain scores. Impact: In knee osteoarthritis the registration of MRIs to an anatomical template significantly increases the association between cartilage volumes and osteoarthritis pain scores, enabling more accurate and sensitive detection of pain-related cartilage changes, potentially influencing OA management and therapy development.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".