Outcomes and Safety of Tumor Necrosis Factor Inhibitors in Reactive Arthritis: A Nationwide Experience from Iceland
Bibliographic record
Abstract
OBJECTIVE: Reactive arthritis (ReA) is a spondyloarthritis triggered by a bacterial infection. In cases where nonsteroidal antiinflammatory drugs and conventional synthetic disease-modifying antirheumatic drugs have failed, biologics such as tumor necrosis factor inhibitors (TNFi) have been used. However, limited evidence exists of the efficacy and safety of these drugs in ReA. We report on Icelandic patients with ReA who have been treated with TNFi, their characteristics, outcomes, and safety. METHODS: We conducted an observational cohort study using the Icelandic nationwide database of biologic therapy (ICEBIO) supplemented with a retrospective study of electronic health record (EHR) data. Drug efficacy was assessed using disease activity scores and standardized questionnaires within ICEBIO; safety was assessed using ICEBIO and EHR data. RESULTS: Thirty-eight patients with ReA were registered in the database. Eight were given TNFi within 1 year of symptom onset. At 6 and 18 months, there was a significant reduction in C-reactive protein (CRP), tender and swollen joints, visual analog scale for pain and fatigue, 28-joint count Disease Activity Score 28 based on CRP, Clinical Disease Activity Index, and Health Assessment Questionnaire scores. Seventy-one to 90% of patients were considered treatment responders. Two patients were able to stop biologics owing to remission. During the 303 patient-years (mean 8, range 1-15) biologics were given, 6 hospital admissions for infections were noted. CONCLUSION: TNFi are safe and effective in ReA, but treatment tends to be prolonged. Further clinical trials are urgently needed in ReA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".