How sensitive and specific are MRI features of sacroiliitis for diagnosis of spondyloarthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the sensitivity and specificity of MRI features of sacroiliitis in spondyloarthritis (SpA). MATERIALS AND METHODS: A retrospective study reviewed MRI of the sacroiliac (SI) joints in 517 patients with inflammatory back pain. Sensitivity, specificity, positive and negative likelihood ratios of active and structural lesions of sacroiliitis with final clinical diagnosis as golden standard was calculated. RESULTS: MRI showed active inflammation in 42% of patients (bone marrow oedema (BMO) (41.5%), capsulitis (3.3%), enthesitis (2.5%)) and structural changes in 48.8% of patients (erosion (25%), fat infiltration (31.6%), sclerosis (32%) and ankylosis (7.6%)). BMO was the MRI feature with the highest sensitivity (65.1%) for diagnosis of SpA. Capsulitis (99%), enthesitis (98.4%), ankylosis (97.4%) and erosion (94.8%) had a high specificity for diagnosis of SpA, whereas BMO (74.3%), sclerosis (75.8%) and fat infiltration (84.0%) were less specific. BMO concomitant with enthesitis, capsulitis or erosions increased the specificity. Concomitant presence of BMO and sclerosis or fat infiltration decreased the specificity. CONCLUSION: BMO is moderately sensitive and specific for diagnosis of SpA in patients with inflammatory back pain. BMO concomitant with enthesitis, capsulitis, ankylosis or erosion increases the specificity. Concomitant fat infiltration or sclerosis decreases the specificity for diagnosis of SpA. Of all lesions, erosion had by far the highest positive likelihood ratio for diagnosis of SpA.
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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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".