The diagnostic utility of MRI in spondyloarthritis: An international multicentre evaluation of 187 subjects (The MORPHO study)
Bibliographic record
Abstract
OBJECTIVE:: To systematically assess the diagnostic utility of MRI to differentiate spondyloarthritis (SpA) patients from patients with non-specific back pain (NSBP) and healthy volunteers using a standardized evaluation of MR images of the sacroiliac joints (SIJ). METHODS:: Five readers blinded to patient and diagnosis independently assessed MRI scans (T1-weighted and STIR sequences) of the SIJ from 187 subjects: 75 patients with AS (symptom duration =10 years); 27 patients with pre-radiographic inflammatory back pain (IBP) (mean symptom duration 29 months); 26 NSBP and 59 healthy controls =45 years. Bone marrow edema (BME), fat infiltration, erosion, and ankylosis were recorded according to standardized definitions using an online data entry system. We calculated sensitivity, specificity, positive (LR+) and negative (LR-) likelihood ratios for the diagnosis of SpA based on global assessment of the MRI scans. RESULTS:: Diagnostic utility was high for all 5 readers for both AS (sensitivity 0.90, specificity 0.97, LR+ 44.6) and pre-radiographic IBP patients (sensitivity 0.51, specificity 0.97, LR+ 26.0). Diagnostic utility based solely on detection of BME enhanced sensitivity (67%) for IBP patients but reduced specificity (88%); detection of erosions in addition to BME further enhanced sensitivity (81%) without changing specificity. A single MRI lesion of the SIJ was observed in up to 27% of control individuals. CONCLUSION:: This systematic and standardized evaluation of SIJ in SpA patients showed that MRI has much greater diagnostic utility than documented previously. We present for the first time a data driven definition of a positive MRI for SpA.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".