Reduced Occurrence Rate of Acute Anterior Uveitis in Ankylosing Spondylitis Treated with Golimumab — The GO-EASY Study
Bibliographic record
Abstract
OBJECTIVE: Acute anterior uveitis (AAU) is common in ankylosing spondylitis (AS). Golimumab (GOL), a tumor necrosis factor-α inhibitor (TNFi), has proven to be effective in the treatment of AS. To date, the effect of GOL on the incidence of AAU in AS is unknown. The objective was to study the AAU occurrence rate in patients with AS during GOL treatment and secondarily, the efficacy of GOL in daily clinical practice. METHODS: The study was a multicenter prospective study in a real-world setting in patients with AS who were treated with GOL for 12 months. The occurrence of AAU was assessed in the year before the initial TNFi treatment and during GOL treatment and calculated for the period at risk for a new AAU. Measures for disease activity [Ankylosing Spondylitis Disease Activity Score (ASDAS)] and treatment response [Assessment of Spondyloarthritis international Society (ASAS20 score)] were collected. RESULTS: In total, 93 patients (65% male, 55% TNFi-naive, 27% history of AAU) were included, with a median disease duration of 7 years and ASDAS score of 3.1. During GOL treatment, the AAU occurrence rate was reduced from 11.1 to 2.2 per 100 patient-years (rate-ratio 0.20, 95% CI 0.04-0.91). After 3 months of treatment, 41% of the patients experienced a clinically important improvement of the ASDAS score (p < 0.001) and 36% an ASDAS20 response (p < 0.001). At month 12, 49% had achieved an ASAS20 response (p < 0.001). CONCLUSION: In AS, the AAU occurrence rate and disease activity decreased significantly during GOL treatment. Therefore, GOL can be considered a good choice in patients with AS who need a TNFi, especially in cases of recurrent AAU. (EudraCT number: 2012-002458-21).
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".