Bilateral Evaluation of the Hand and Wrist in Untreated Early Inflammatory Arthritis: A Comparative Study of Ultrasonography and Magnetic Resonance Imaging
Bibliographic record
Abstract
OBJECTIVE: To compare Doppler ultrasound (US) and 3.0-Tesla magnetic resonance imaging (3.0-T MRI) findings of synovial inflammation in the tendons and joints in an early polyarthritis cohort (patients who presented < 1 year after arthritis onset) using a bilateral hand and wrist evaluation. Also, to evaluate the diagnostic performance of US and MRI findings for rheumatoid arthritis (RA), their ability to predict RA as a diagnostic outcome, and their capacity to improve the accuracy of the 2010 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) RA classification criteria in early arthritis. METHODS: Forty-five patients (40 women, 5 men; mean age 45.6 yrs) with untreated recent-onset polyarthritis participated in this prospective study and were examined using an US and MRI approach including both wrists and hands. After a followup of 12 months, patients were classified as having RA if they fulfilled the criteria for RA. The proportion of synovitis identified by US and MRI for each joint and tendon region was compared by chi-square test. The diagnostic performance of US and MRI for RA identification was evaluated using receiver-operating curve (ROC) analysis. Possible associations between synovitis for each joint and tendon region as identified by US or MRI and RA diagnosis at 12 months were tested by logistic regression analysis. The diagnostic performance of the ACR/EULAR RA classification criteria corrected by US and MRI joint and tendon counts was evaluated using ROC analysis. RESULTS: Thirty patients fulfilled the ACR/EULAR criteria [early RA (ERA) patients] and the remaining 15 failed to meet these criteria (non-RA). Carpal joint synovitis and tenosynovitis of the flexor tendons was found in 86.7% and 86.7% of patients with ERA on MRI compared with 63.3% and 50% on US, respectively (p < 0.05). The global MRI and US counts revealed a good diagnostic performance for RA diagnosis of both techniques, although MRI was statistically significantly better [area under the curve (AUC) = 0.959 and AUC = 0.853, respectively; z statistic = 2.210, p < 0.05]. MRI identification of carpal joint synovitis (OR 3.64, 95% CI 1.119-11.841), tenosynovitis of the flexor tendons (OR 5.09, 95% CI 1.620-16.051), and global joint and tendon count (OR 2.77, 95% CI 1.249-6.139) were in the multivariate logistic regression model the most powerful predictors of progression toward RA. In the group of ERA patients with US joint and tendon counts ≤ 10, a statistically significant difference was found between the diagnostic performance for RA of the ACR/EULAR criteria as previously described and the diagnostic performance of the MRI-corrected ACR/EULAR criteria (AUC = 0.898 and AUC = 0.986, respectively; z statistic = 2.181, p < 0.05). CONCLUSION: 3.0-T MRI identified a higher prevalence of synovitis in comparison to US in an early polyarthritis cohort. Both techniques have good diagnostic performance for RA although MRI reveals a significantly higher diagnostic capability. Synovitis of carpal joints and of flexor tendons as identified by MRI were the most powerful predictors of progression toward RA. In patients with US joint and tendon counts ≤ 10, MRI can significantly improve the diagnostic performance of the 2010 ACR/EULAR classification criteria.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".