Associations Of Abnormalities Detected On Mri And Radiography With Hand Pain And Function In A Population-Based Older Adult Cohort.
Bibliographic record
Abstract
Abstract Objective To describe associations between hand abnormalities on MRI or radiographs (X-ray) and pain and function in a cross-sectional study of community-based older adults. Methods Distal and proximal interphalangeal index finger joints (n=221) were examined using MRI, X-ray, and hand examination. Hand pain, function, and stiffness were assessed using Australian/Canadian hand osteoarthritis index (AUSCAN) questionnaire. Grip strength was assessed using dynamometer. Models were adjusted for age, sex, and other MRI or X-ray abnormalities. Results Absence of collateral ligament (CLs) on MRI (relative risk; RR=3.15 (95% confidence interval 1.33, 7.50), and joint space narrowing (JSN) on X-ray (RR=2.96 (1.33, 6.58)) was associated with having a painful joint after adjustment for confounders. JSN was also associated with tender joints (RR=2.19 (1.01, 4.76)). Effusion-synovitis was associated with better AUSCAN pain scores (OR=0.51 (0.28, 0.94)) and JSN with worse AUSCAN pain scores (odds ratio; OR=1.67 (1.13, 2.48)). Absent CLs were also associated with stiffer joints (OR=3.12 (1.26, 7.70)) and weaker grip strength (β=-1.69 (-2.95, -0.43)) independent of pain and other features; JSN was also associated with weaker grip strength (β=-0.87 (-1.62, -0.14)). No other MRI or X-ray abnormalities were associated with pain or function independent of age, sex or pain. Conclusion Most MRI abnormalities were not associated with pain and function cross-sectionally. Absent CLs and JSN were associated with painful joints and weak grip strength independent of pain and other imaging features. JSN was also associated with tender joints and absent CLs with stiff joints. Unexpectedly, effusions were associated with reduced odds of pain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".