Comparison of the effects of rituximab, tumor necrosis factor alpha inhibitors and non-steroidal antiinflammatory drugs on ankylosing spondylitis clinical activity and sacroileitis intensity on magnetic resonance imaging
Bibliographic record
Abstract
Aim. To investigate the effect of rituximab in ankylosing spondylitits on disease activity and on intensity of sacroileitis detected by magnetic resonance imagigng compared to tumor necrosis factor alpha inhibitors and non-steroidal anti-inflammatory drugs. Methods. The study included 91 patient [14 (15.4%) females, 77 (84.6%) males, mean age - 34.4±9.13 years, disease duration - 6.6±3.8 years] with established diagnosis of ankylosing spondylitits. The main group included 20 patients (17 males, mean age 36.9±9.9 years) who were treated with rituximab. The comparison group included 36 patients (30 males, mean age 34.3±8.6 years) treated with tumor necrosis factor alpha inhibitors. The control group consisted of 35 patients (30 males, mean age 33.4±9.3 years) treated with non-steroidal anti-inflammatory drugs and sulfasalazine. Disease activity was measured by BASDAI (Bath Ankylosing Spondylitis Disease Activity Index) and ASDASESR (Ankylosing Spondylitis Disease Activity Score) scores before the treatment initiation, at the 2nd, 8th, 16th and 24th weeks of treatment. Magnetic resonance imaging of sacroiliac joints was performed before the treatment initiation and at the 24th week of treatment, T1 and STIR sequences were analyzed. Results were processed using the SPARCC (Spondyloarthritis Research Consortium of Canada) scoring methodology. Results. There was a significant reduction of the disease clinical activity measured by BASDAI score (from 6.19±1.48 to 3.70±0.91, p 0.01) and ASDASESR score (form 3.43±0.72 tо 2.11±0.46, p 0.01) in patients treated with rituximab at the week 24. Mean SPARCC score reduced from 15.9±7.2 to 4.6±8.2 (p 0.01). The influence of rituximab on clinical activity of the disease was superior over the effect of the standard treatment (BASDAI and SPARCC scores 5.22±1.14 and 7.8±7.1 accordingly at the week 24, p 0.05), but inferior over the effect of tumor necrosis factor alpha inhibitors (BASDAI and SPARCC scores 2.03±0.64 и 4.9±7.0 accordingly at the week 24, p 0.01). Conclusion. Anti B-cell therapy is effective in treating active ankylosing spondylitis and leads to the decrease of the clinical symptoms intensity and disease activity measured by BASDAI и ASDASESR scores. The effect of rituximab was superior over the effect of non-steroidal anti-inflammatory drugs and sulfasalazine and inferior over the effect of tumor necrosis factor alpha inhibitors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".