Long-term safety and effectiveness of tumour necrosis factor inhibitors in systemic sclerosis patients with inflammatory arthritis.
Bibliographic record
Abstract
OBJECTIVES: To assess the long-term safety and effectiveness of tumour necrosis factor (TNF) inhibitors in the treatment of systemic sclerosis (SSc) patients with inflammatory arthritis. METHODS: SSc patients who fulfilled the ACR criteria and had inflammatory arthritis followed in The Scleroderma Programme at the Mount Sinai and Toronto Western Hospitals, Toronto, Canada who received a TNF inhibitors for 12 months or more were retrospectively reviewed. Safety outcomes included development of TNF inhibitor related side effects, malignancy and death. Effectiveness outcomes included swollen joint count, tender joint count, skin score, and self-reported pain score at 12 months, compared to baseline. RESULTS: Ten SSc patients were identified: 7 (70%) were female and 6 (60%) had diffuse disease with a median skin score of 6. Six patients (60%) had ILD. At 12 months, the median swollen joint count and tender joint count significantly decreased from 10 to 0 (p<0.01) and 15 to 3 (p=0.02), respectively. The median pain score decreased from 6 to 3.5 (p=0.10). The median skin score remained unchanged at 6 months. The FVC and DLCO changed from 86% and 65% respectively, to 80% and 75% respectively. One patient developed uncomplicated herpes zoster. After 30 months, 3 patients (30%) developed malignancy. No death or other adverse events were observed. CONCLUSIONS: TNF inhibitors appear to be effective in the treatment of SSc-associated inflammatory arthritis. Skin score and lung function did not change significantly with therapy. However, malignancy occurred in one third of patients. Further studies are required to confirm these findings.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".