Discrepancies between routine sacroiliac joint MRI reporting and current expert recommendations in patients with spondyloarthritis
Bibliographic record
Abstract
BACKGROUND: Sacroiliac joint (SIJ) MRI is commonly used in diagnosing spondyloarthritis (SpA). Current Assessment of SpondyloArthritis International Society (ASAS) recommendations on reporting SIJ MRIs in patients with known or suspected axial SpA recommend always stating whether bone marrow oedema (BME), erosions and fat lesions are present/absent and whether the MRI is compatible with axial SpA. PURPOSE: To investigate if routine care radiologists already report what has now been recommended and to assess the agreement between local radiologists and central SpA experts. MATERIALS AND METHODS: This study includes retrospective interpretation of images acquired in routine care. Patients diagnosed with SpA enrolled in a clinical registry in one of five European countries involved in the EuroSpA Collaboration, with an available SIJ MRI and an associated local MRI report, were included. MRIs were read centrally by two readers, who registered global features (eg, MRI indicative of SpA), and various inflammatory and structural lesions as present/absent. Similar information was extracted from local reports. Findings were analysed with descriptive statistics. RESULTS: Overall, 913 patients (40 years±13, 492 men) were included. In 24%, the local MRI reports stated whether the MRI was overall indicative of SpA or not. Presence/absence of BME, erosions and fat lesions was mentioned in 88%, 48% and 29% of local reports, respectively. Inflammatory lesions were more often reported as present by local than central readers (46% vs 36%), and structural lesions less often (33% vs 50%). CONCLUSION: This study demonstrated a large gap between the clinical practice of reporting SIJ MRIs and recent reporting recommendations.
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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 | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".