Factors Influencing Concordance Between Clinical and Ultrasound Findings in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Clinical joint examination (CJE) is less time-consuming than ultrasound (US) in rheumatoid arthritis (RA). Low concordance between CJE and US would indicate that the 2 tests provide different types of information. Knowledge of factors associated with CJE/US concordance would help to select patients and joints for US. Our objective was to identify factors associated with CJE/US concordance. METHODS: Seventy-six patients with RA requiring tumor necrosis factor-α (TNF-α) antagonist therapy were included in a prospective, multicenter cohort. In each patient, 38 joints were evaluated. Synovitis was scored using CJE, B-mode US (B-US), and power Doppler US (PDUS). Joints whose kappa coefficient (κ) for agreement CJE/US was < 0.1 were considered discordant. Multivariate analysis was performed to identify factors independently associated with CJE/US concordance, defined as factors yielding p < 0.05 and OR > 2. RESULTS: Concordance before TNF-α antagonist therapy varied across joints for CJE/US (κ = -0.08 to 0.51) and B-US/PDUS (κ = 0.30 to 0.67). CJE/US concordance was low at the metatarsophalangeal joints and shoulders (κ < 0.1). Before TNF-α antagonist therapy, a low 28-joint Disease Activity Score (DAS28) was associated with good CJE/B-US concordance, and no factors were associated with CJE/PDUS concordance. After TNF-α antagonist therapy, only the joint site was associated with CJE/B-US concordance; joint site and short disease duration were associated with CJE/PDUS concordance. CONCLUSION: Concordance between CJE and US is poor overall. US adds information to CJE, most notably at the metatarsophalangeal joints and shoulders. Usefulness is decreased for B-US when DAS28 is low and for PDUS when disease duration is short.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".