Can erosions on MRI of the sacroiliac joints be reliably detected in patients with ankylosing spondylitis? - A cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: Erosions of the sacroiliac joints (SIJ) on pelvic radiographs of patients with ankylosing spondylitis (AS) are an important feature of the modified New York classification criteria. However, radiographic SIJ erosions are often difficult to identify. Recent studies have shown that erosions can be detected also on magnetic resonance imaging (MRI) of the SIJ early in the disease course before they can be seen on radiography. The goals of this study were to assess the reproducibility of erosion and related features, namely, extended erosion (EE) and backfill (BF) of excavated erosion, in the SIJ using a standardized MRI methodology. METHODS: Four readers independently assessed T1-weighted and short tau inversion recovery sequence (STIR) images of the SIJ from 30 AS patients and 30 controls (15 patients with non-specific back pain and 15 healthy volunteers) ≤ 45 years old. Erosions, EE, and BF were recorded according to standardized definitions. Reproducibility was assessed by percentage concordance among six possible reader pairs, kappa statistics (erosion as binary variable) and intraclass correlation coefficient (ICC) (erosion as sum score) for all readers jointly. RESULTS: SIJ erosions were detected in all AS patients and six controls by ≥ 2 readers. The median number of SIJ quadrants affected by erosion recorded by four readers in 30 AS patients was 8.6 in the iliac and 2.1 in the sacral joint portion (P < 0.0001). For all 60 subjects and for all four readers, the kappa value for erosion was 0.72, 0.73 for EE, and 0.63 for BF. ICC for erosion was 0.79, 0.72 for EE, and 0.55 for BF, respectively. For comparison, the kappa and ICC values for bone marrow edema were 0.61 and 0.93, respectively. CONCLUSIONS: Erosions can be detected on MRI to a comparable degree of reliability as bone marrow edema despite the significant heterogeneity of their appearance on MRI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".