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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".