Clinical importance of inflammatory facet joints of the spine in ankylosing spondylitis: a magnetic resonance imaging study
Bibliographic record
Abstract
OBJECTIVES: The aims of this study were to assess the reliability of a novel magnetic resonance imaging (MRI) scoring system for inflammatory lesions of facet joints and to clarify the clinical significance of facet joint inflammation in ankylosing spondylitis (AS). METHOD: A total of 53 AS patients (45 males, 84.9%) were assessed for active inflammatory lesions involving the facet joints, as indicated by bone marrow oedema, at 23 discovertebral units (DVUs) between C2 and S1 using a novel scale, the AS Activity of the Facet joint (ASAFacet). The reliability of the ASAFacet was evaluated using intraclass correlation coefficients (ICCs) and Bland-Altman plots. RESULTS: ICC values for the ASAFacet scores were 0.857 [95% confidence interval (CI) 0.741-0.919] for inter-observer and 0.941 (95% CI 0.873-0.969) for intra-observer reliability. Inflammatory activity scores in facet joints were evenly distributed at all spine levels (p = 0.294 for ASAFacet), whereas vertebral body inflammation was more prominent in the thoracic spine than in the cervical and lumbar spine [p < 0.001 for the AS spine MRI activity (ASspiMRI-a) score, p = 0.002 for the Berlin method, and p < 0.001 for the Spondyloarthritis Research Consortium of Canada (SPARCC) MRI index]. ASAFacet scores were closely associated with erythrocyte sediment rate (ESR) and C-reactive protein (CRP) levels (p < 0.05, respectively). Patients with peripheral arthritis had fewer lesions involving the vertebral bodies or facet joints than patients without peripheral arthritis (p < 0.001 for the four different MRI activity indexes). CONCLUSIONS: This study suggests that recognition of facet joint inflammation has the potential to contribute to our understanding of clinical outcomes in AS.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".