Long-term Follow-up and Optimization of Infliximab in Refractory Uveitis Due to Behçet Disease: National Study of 103 White Patients
Bibliographic record
Abstract
OBJECTIVE: In a large series of White patients with refractory uveitis due to Behçet disease (BD) being treated with infliximab (IFX), we assessed (1) long-term efficacy and safety of IFX, and (2) IFX optimization when ocular remission was achieved. METHODS: Our multicenter study of IFX-treated patients with BD uveitis refractory to conventional immunosuppressant agents treated 103 patients/185 affected eyes with IFX as first biologic therapy in the following intervals: 3-5 mg/kg intravenous at 0, 2, 6, and then every 4-8 weeks. The main outcome variables were analyzed at baseline, first week, first month, sixth month, first year, and second year of IFX therapy. After remission, based on a shared decision between patient and clinician, IFX optimization was performed. Efficacy, safety, and cost of IFX therapy were evaluated. RESULTS: pneumonia (n = 1), severe oral ulcers (n = 1), palmoplantar psoriasis (n = 1), and colon carcinoma (n = 1). In the optimization subanalysis, the comparative study between optimized and nonoptimized groups showed (1) no differences in clinical characteristics at baseline, (2) similar maintained improvement in most ocular outcomes, (3) lower severe adverse events, and (4) lower mean IFX costs in the optimized group (€4826.52 vs €9854.13 per patient/yr). CONCLUSION: IFX seems to be effective and relatively safe in White patients with refractory BD uveitis. IFX optimization is effective, safe, and cost-effective.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".