The utility of magnetic resonance imaging lesion combinations in the sacroiliac joints for diagnosing patients with axial spondyloarthritis. A prospective study of 204 participants including post-partum women, patients with disc herniation, cleaning staff, runners and healthy persons
Bibliographic record
Abstract
OBJECTIVES: To investigate the diagnostic utility of different combinations of SI joint MRI lesions for differentiating patients with axial SpA (axSpA) from other conditions with and without buttock/pelvic pain. METHODS: A prospective cross-sectional study included patients with axSpA (n = 41), patients with lumbar disc herniation (n = 25), women with (n = 46) and without (n = 14) post-partum (birth within 4-16 months) buttock/pelvic pain and cleaning assistants (n = 26), long-distance runners (n = 23) and healthy men (n = 29) without pain. Two independent readers assessed SI joint MRI lesions according to the Spondyloarthritis Research Consortium of Canada MRI definitions and pre-defined MRI lesion combinations with bone marrow oedema (BME) and fat lesions (FAT), respectively. Statistical analyses included the proportion of participants with scores above certain thresholds, sensitivity, specificity, positive and negative predictive values and likelihood ratios. RESULTS: BME adjacent to the joint space (BME@joint space) was most frequent in axSpA (63.4%), followed by women with post-partum pain (43.5%), but was present in nearly all groups. BME adjacent to fat lesions (BME@FAT) and BME adjacent to erosions (BME@erosion) were only present in axSpA patients and in women with post-partum pain, but scores ≥3 and ≥4, respectively, were only seen in axSpA patients. FAT@erosion was exclusively recorded in axSpA patients. FAT@joint space and FAT@sclerosis were present in most groups, but with higher scores in the axSpA group. CONCLUSION: BME@joint space and FAT@joint space were frequent in axSpA but also in other conditions, reducing the diagnostic utility. FAT@erosion, and BME@FAT, BME@erosion and FAT@sclerosis above certain thresholds, were exclusively seen in axSpA patients and may thus have diagnostic utility in the differentiation of axSpA from other conditions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".