Grading of Inflammatory Disease Activity in the Sacroiliac Joints with Magnetic Resonance Imaging: Comparison Between Short-tau Inversion Recovery and Gadolinium Contrast-enhanced Sequences
Bibliographic record
Abstract
OBJECTIVE: We investigated the potential concordance of 2 different magnetic resonance (MR) sequences - short-tau inversion recovery (STIR) and fat-saturated T1-weighted spin-echo after application of gadolinium (Gd) contrast medium to detect active bone marrow abnormalities at the sacroiliac joints (SIJ) in patients with spondyloarthritis (SpA). METHODS: Blinded and using the Danish scoring method, we evaluated transaxial MR images of the 2 sequences in 40 patients with SpA with disease duration of 3-14 years. Both the cartilaginous and ligamentous portions of the SIJ were analyzed. RESULTS: There was a significant positive correlation between the activity scores obtained by STIR and Gd-enhanced sequences (p < 0.0001). Agreement in the detection of bone marrow abnormalities occurred in 60 of the 80 joints, 35 with and 25 without signs of active disease. Discordance with STIR-positive marrow activity scores occurred in only 11 joints; Gd-enhanced positive scores in 9 joints. The STIR sequence detected remnants of marrow activity in the periphery of chronic fatty replacement not seen or partly obscured on the Gd sequence. Small subchondral enhancing lesions may not be scored on the STIR sequence, mostly because of reduced image resolution. CONCLUSION: Active bone marrow abnormalities were detected nearly equally well with STIR and Gd-enhanced fat-suppressed T1 sequences in patients with SpA, with STIR being most sensitive to visualize active abnormalities in the periphery of chronic changes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".