Detection of Sacroiliitis by Short-tau Inversion Recovery and T2-weighted Turbo Spin Echo Sequences: Results from the SIMACT Study
Bibliographic record
Abstract
OBJECTIVE: To compare proton density-weighted short-tau inversion recovery (PD-STIR) and T2-weighted fat-suppressed turbo spin echo (T2-FS) sequences for detecting osteitis lesions of the sacroiliac joints (SIJ) in patients with chronic low back pain (CLBP). METHODS: This prospective study included 110 patients with CLBP and suspected spondyloarthritis and 18 healthy controls. All 128 participants (age range: 19-57 yrs) underwent 3.0 Tesla magnetic resonance imaging (MRI) of the SIJ including PD-STIR and T2-FS. Two readers independently scored PD-STIR and T2-FS images for osteitis in separate sessions. Sum scores and signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were calculated. Images were further analyzed as to whether they fulfilled the Assessment of SpondyloArthritis international Society (ASAS) criterion of a positive MRI (MRI+). Interreader agreement was calculated using intraclass correlation coefficients. RESULTS: Average osteitis sum scores were higher for T2-FS images (mean sum score of 4.10 in T2-FS vs 2.55 in PD-STIR, p = 0.017). Mean SNR was 16.54 for PD-STIR and 37.30 for T2-FS (p = 0.0289). Mean CNR was 4.14 for PD-STIR and 20.20 for T2-FS (p = 0.0212). For both readers, the ASAS MRI+ definition was more often fulfilled by T2-FS than by PD-STIR images, resulting in more patients being classified as having axial spondyloarthritis (axSpA): 68 patients using T2-FS versus 58 patients using PD-STIR. Interreader intraclass correlation coefficients were very good for both PD-STIR (0.91) and T2-FS (0.86). CONCLUSION: T2-FS sequences improve image quality and hence the detection of osteitis compared to the PD-STIR sequence. More patients were classified as axSpA based on a positive MRI by T2-FS.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".