Use it or lose it: The relationship between two image-based biomarkers in better understanding osteoarthritis progression in the wrist
Bibliographic record
Abstract
Bone tissue is influenced by its mechanical environment and adapts in response to its mechanical load. This is supported by studies analyzing bone adaptation in the knee and hip. Changes to the bone have also been found to precede cartilage degeneration in diseases such as osteoarthritis (OA). Our objective was to demonstrate the relationship between joint contact and bone density in the wrists of healthy adults. Static CT scans with a calibration phantom were taken to obtain measures of bone mineral density (vBMD) in 3 normalized depths; 0 - 2.5, 2.5 - 5, and 5 - 7.5 mm. Participants underwent a four-dimensional CT scan (4DCT) while performing maximum wrist extension to maximum wrist flexion. 3D bone models of the distal radius, scaphoid, and lunate were made, and analyzed vBMD and joint contact area (JCA) in the radiolunate (RL) and radioscaphoid (RS) joints separately. Correlation coefficients were calculated where vBMD was the dependent variable, and kinematic JCA throughout every 10 degrees of motion were the independent variables. Statistically significant independent variables associated with vBMD were assessed using a regression model and were entered in steps; (1) significant correlations, (2) sex, and (3) age.An increase in vBMD was significantly, positively associated with an increase in JCa. Notably, in the deeper regions (5 - 7.5 mm) of the radius that is primarily composed of trabecular bone. Sex contributed to the variance in vBMD, while age did not. Subchondral bone changes are influenced by wrist position, demonstrating that the wrist serves to bear load similar to the knee and hip.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".