The Value of Magnetic Resonance Imaging and Ultrasound in Undifferentiated Arthritis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic literature review of the diagnostic and prognostic value of magnetic resonance imaging (MRI) and ultrasound (US) in patients with undifferentiated peripheral inflammatory arthritis (UPIA), and to assess if MRI and US should be done at baseline and repeated, and if so, at what interval. METHODS: Medline, Embase, the Cochrane Library, and abstracts presented at the 2007 and 2008 meetings of the American College of Rheumatology and European League Against Rheumatism meetings were searched for diagnostic and prognostic studies of any duration examining the ability of MRI/US to predict outcome of patients with UPIA. Sensitivity, specificity, predictive values, and positive/negative likelihood ratios (LR+/LR-) were calculated. When available, odds ratios were extracted. Quality was appraised using validated scales. RESULTS: Regarding MRI, 11 out of 2595 screened references were included: 2 described pure undifferentiated arthritis (UA) populations and 9, mixed populations. Bone edema (LR+ 4.5) and combination of a distinct MRI synovitis and erosion pattern (LR+ 4.8) increased probability of developing rheumatoid arthritis (RA). Absence of MRI synovitis (LR- 0.2) and absence of a distinct synovitis pattern (LR- 0) decreased probability of developing RA. Regarding US, 2 out of 2111 references were included, both mixed populations; no data could be extrapolated for UPIA. CONCLUSION: MRI bone edema and combined synovitis and erosion pattern seem useful in predicting development of RA from UPIA. The value of US in UPIA remains to be determined. The absence of MRI synovitis seems useful in excluding development of RA. No data were found about the value of repeating MRI/US. Studies evaluating MRI/US in UPIA are scarce, but current knowledge strongly encourages further testing in UA.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".