Impact of tumor necrosis factor α inhibitors on MRI inflammation in axial spondyloarthritis assessed by Spondyloarthritis Research Consortium Canada score: A meta-analysis
Bibliographic record
Abstract
Spondyloarthritis Research Consortium Canada (SPARCC) score is an effective magnetic resonance imaging (MRI) evaluation method for inflammation in axial spondyloarthritis. Previously published meta-analyses have shown tumor necrosis factor α inhibitors (TNFi) had great effectiveness on improving disease activity and function in axial spondyloarthritis. However, there still has no one that concentrates on the impact of TNFi on MRI inflammation. We conduct a meta-analysis to summarize the impact of TNFi on MRI inflammation in axial spondyloarthritis using SPARCC score. Comprehensive search was conducted in the databases of OVID Medline, OVID EMBASE, and Cochrane library on November 14, 2020. We investigated the differences in SPARCC score of sacroiliac joint and spine, before and after TNFi treatment in patients with axial spondyloarthritis. SPARCC score was further compared in the subgroup by diagnostic category and TNFi types. In addition, clinical assessment indicators including ankylosing spondylitis disease activity score, bath ankylosing spondylitis disease activity index, bath ankylosing spondylitis functional index, c-reactive protein were also analyzed. Data were pooled by mean differences (MD) with 95% confidence intervals (CI) and publication bias was assessed by Egger's test. Jadad scale was applied to assess the quality of included trials. Compared with control group, TNFi significantly improved SPARCC score of sacroiliac joints (n = 11, MD = 2.86, 95% CI 2.50, 3.23) and spine (n = 5, MD = 1.87,95%CI 1.27, 2.46). This effect was consistent among subgroups by different diagnostic category (ankylosing spondylitis, non-radiographic axial spondyloarthritis) and TNFi types (adalimumab, certolizumab pegol). Analysis of clinical assessment indicators also confirmed the therapeutic effect on axial spondyloarthritis. Egger's test suggested no possibility of publication bias. This meta-analysis shows that TNFi are effective to improve MRI inflammation in patients with axial spondyloarthritis and the treatment effectiveness is not affected by diagnostic category and TNFi types.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.052 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".