The role of radiology in the quantification of digital ulnar deviation in rheumatoid arthritis patients
Bibliographic record
Abstract
BACKGROUND: Rheumatoid arthritis (RA) is a common inflammatory polyarthritis, which causes functional digital ulnar deviation (UD). Radiographic and magnetic resonance imaging (MRI) assessment of the hands is essential in RA, but its role in the quantification of UD remains unclear. PURPOSE: To compare UD measurements in RA patients between clinical goniometric assessments versus standardized radiographs and MRI. METHODS: Fifteen RA patients with clinically apparent UD and 11 RA patients without UD underwent a rheumatological examination prior to recruitment to this study. Goniometric measurements for UD at the metacarpophalangeal (MCP) joints were performed by an occupational therapist (OT). Standardized hand radiographs, and MRI studies of the dominant hand using 3T MRI scanner with 16 channel hand/wrist coil were evaluated. Angulation measurements for radiographs and MRI were performed independently by two experienced musculoskeletal radiologists who were blinded to the rheumatologist's, occupational therapist's and each other's assessments. RESULTS: Inter-observer correlation between radiologists was >0.97 for both radiographic and MRI measurements. Correlation between OT goniometric measurements and the imaging-based measurements was limited at 0.496 for radiographs and 0.317 for MRI. Correlation between imaging modalities was 0.513. Compared to OT measurements, radiographic and MRI study measurements significantly underestimate the angulation in RA patients with UD (P < 0.001). CONCLUSIONS: The results of this study demonstrated discordance between radiological and goniometric measurements of digital ulnar angulation at the MCP joints in RA patients. Although imaging plays a key role in understanding structural damage and disease activity in RA, it should be emphasized that radiological measurements underrate joint malalignment.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".