Low-dose Infliximab (3 mg/kg) Significantly Reduces Spinal Inflammation on Magnetic Resonance Imaging in Patients with Ankylosing Spondylitis: A Randomized Placebo-controlled Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the influence of low-dose infliximab (IFX) on spinal inflammation scored by magnetic resonance imaging (MRI). The dose recommended for rheumatoid arthritis (3 mg/kg) is also clinically effective for ankylosing spondylitis (AS), although effects on spinal inflammation as defined by MRI have yet to be described in a placebo-controlled trial. METHODS: In a 12-week double-blind period, patients were randomized 1:1 to receive either IFX 3 mg/kg at 0, 2, and 6 weeks, or placebo. Spinal inflammation in discovertebral units (DVU) was measured by the Spondyloarthritis Research Consortium of Canada (SPARCC) MRI Index at baseline and 12 weeks by 3 readers blinded to timepoint and treatment allocation. We also compared reliability and discrimination of the SPARCC MRI index based on evaluation of the entire spine (23 DVU score) compared to assessment of only the 6 most severely affected DVU (6 DVU score). RESULTS: At Week 12, patients treated with IFX experienced mean reductions of 55.1% and 57.2% in the 6 DVU and 23 DVU SPARCC scores, respectively, compared with a mean increase of 5.8% and decrease of 3.4% in 6 DVU and 23 DVU scores, respectively, for patients taking placebo (p < 0.001). A large treatment effect (Guyatt's effect size >or= 1.7) and high reliability was evident and comparable between 6 DVU and 23 DVU scoring methods. CONCLUSION: Treatment with low-dose IFX leads to a large treatment effect on spinal inflammation as measured by MRI. Scoring for inflammation of only the most severely affected regions of the spine by MRI is comparable to assessment of the entire spine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".