Effect of Characteristic Inflammatory and Structural Pelvic Magnetic Resonance Imaging Lesions on Expert Assessment of Axial Juvenile Spondyloarthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the influence of pelvic magnetic resonance imaging (MRI) findings on axial disease assessment in juvenile spondyloarthritis (JSpA). METHODS: This was a cross-sectional study of patients with JSpA with suspected axial disease. Three experts reviewed each case and rated their confidence (-3 to +3) in the presence of axial disease, first with clinical data and second with clinical and MRI data. Agreement was defined as ≥ 2/3 clinical experts with a rating of ≤ -1 or ≥ 1, and high confidence agreement as ≤ -2 or ≥ 2. The association of clinical features and both global assessments was tested with modified Poisson regression models. RESULTS: Two hundred seventy-two of 303 cases (89.8%) achieved agreement with clinical data alone. Adding imaging data affected agreement in 38.9% (118/303) and directionality of agreement in 23.4% (71/303). Agreement was facilitated in 26/31 cases and lost in 21/272 cases. Of those 71 cases that changed directionality, 33 changed from axial disease being absent to present and 38 from present to absent. The final model had an area under the receiver-operating characteristic (AUROC) curve of 0.93 and 3 factors were independently associated with expert agreement (HLA-B27: relative risk [RR] 1.41, 95% CI 1.14-1.74; pain improvement with activity: RR 1.27, 95% CI 1.05-1.54; and bone marrow edema on MRI: RR 4.08, 95% CI 2.91-5.73). CONCLUSION: The addition of imaging data affected directionality and improved high confidence agreement of expert assessment of axial disease. These results underscore the integral role of MRI in the determination of axial disease in JSpA.
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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 | 0.019 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".