Evaluation of treatments for sacroiliitis in spondyloarthropathy using the Spondyloarthritis Research Consortium Canada scoring system
Bibliographic record
Abstract
BACKGROUND: In this study, the Spondyloarthritis Research Consortium Canada (SPARCC) scoring method was used to compare treatment methods in patients with axial spondyloarthritis (SpA), a form of sacroiliitis. MRI abnormalities in bone marrow edema (BME) were compared before and after treatment in order to compare the efficacy of anti-TNF-α and DMARD, alone or in combination, as treatments for sacroiliitis. METHODS: Fifty-six Chinese patients with axial SpA (mean age 22.6 years) were recruited. Patients were divided into three groups according to different treatments (anti-TNF-α alone vs. DMARDs alone vs. combined anti-TNF-α and DMARDs). MRI examinations were performed before and after treatment. The SPARCC score, clinically relevant AS Disease Activity (ASDAS) indices, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) were analyzed. RESULTS: After treatment, ASDAS and SPARCC scores, ESR, and CRP were significantly improved (P < 0.05) in the anti-TNF-α monotherapy and combination groups; however, there were no statistically significant differences (P > 0.05) in clinical disease activity and radiological inflammation of sacroiliac joint (SIJ) in patients in the DMARDs alone group. SPARCC showed a correlation with ASDAS score pre-treatment, but not post-treatment. Furthermore, there were significant changes (P < 0.05) in these patients with axial SpA after only 3 months of treatment. Follow-up studies of patients who continued therapy for 4-6 months and 9-12 months revealed statistically significant differences from baseline (P < 0.05). CONCLUSIONS: SPARCC can be used to assess severity of disease pre-treatment. Anti-TNF-α treatment resulted in effective reduction of disease activity and BME of SIJ after 3 months of therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".