MRI Detection of Active Sacroiliitis in First Degree Relatives of Ankylosing Spondylitis Patients with Clinical and Laboratory Correlations
Bibliographic record
Abstract
Abstract Background. Detection of ankylosing spondylitis (AS) in the preclinical stage could help prevent long term morbidity in this patients’ population. The aim of this study was to examine the prevalence of active sacroiliitis in first-degree relatives of AS patients using MRI with clinical and laboratory correlations as these patients may benefit from MRI screening and early treatment.Methods. Seventeen first-degree relatives of AS patients were recruited prospectively. AS screening questionnaires (Ankylosing Spondylitis Disease Activity Score, Bath Ankylosing Spondylitis Disease Activity Index & Visual Analogue Scale), blood tests (C-Reactive Protein, HLA-B27), and an MRI of the SIJs were taken. Two musculoskeletal radiologists interpreted the MRI scans, and two physiotherapists applied four symptom provocation tests (Gaenslen's test, posterior pelvic pain provocation test, Patrick's Faber (PF) test and palpation of the long dorsal SIJ ligament test), and two functional movement tests (active straight leg raise and Stork test). Results. Seven (41%) of the 17 participants demonstrated MRI evidence of active sacroiliitis. Of the 7 participants with active sacroiliitis, two (29%) had no history of recent low back pain (LBP), two (29%) had negative HLA-B27, and one (14%) participant had neither back pain nor positive HLA-B27. The Cohen's Kappa score for the interobserver agreement between the radiologists was 1.00 (p-value <0.0001). Despite fair to strong between therapist agreement for the physical test outcomes (Kappa 0.26 to 1.00), the physical test results per se did not have any predictive association with a positive MRI.Conclusions. MRI detected active sacroiliitis in 41% of first-degree relatives of AS patients. The lack of a history of prior LBP or positive HLA-B27 in active sacroiliitis participants might suggest that MRI screening for this high-risk population is warranted; however, further larger studies are needed to help elucidate its cost-effectiveness and long-term benefits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".