Incidence of Uveitis in Secukinumab‐treated Patients With Ankylosing Spondylitis: Pooled Data Analysis From Three Phase 3 Studies
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to report the incidence of uveitis in secukinumab-treated patients with ankylosing spondylitis (AS) in a pooled analysis of three phase 3 trials (MEASURE 1-3 [ClinicalTrials.gov identifiers NCT01358175, NCT01649375, NCT02008916]). METHODS: Analysis included pooled patient-level data from all patients (N = 794) who received any dose (one or more) of secukinumab up to the last patient attending the week 156 study visit in MEASURE 1 and up to the week 156 visit in MEASURE 2 and the week 104 visit in MEASURE 3 for each patient. Postmarketing data were from the periodic safety update report. Incidence of uveitis is reported as the exposure-adjusted incidence rate (EAIR) per 100 patient-years of secukinumab exposure. RESULTS: Overall, 135 (17%) patients reported preexisting (but not active or ongoing) uveitis at baseline, and 589 (74.2%) patients were HLA antigen B27 positive. The EAIR for uveitis was 1.4 per 100 patient-years over the entire treatment period. Among all cases of uveitis (n = 26), 14 (54%) were flares. The exposure-adjusted reporting rate of uveitis in the postmarketing data (which included patients across the three approved indications of psoriasis, psoriatic arthritis, and AS) was 0.03 per 100 patient-years based on cumulative secukinumab exposure of 96 054 patient-years. CONCLUSION: The incidence rate of uveitis in secukinumab-treated patients with active AS does not suggest an increased risk with secukinumab treatment.
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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 | 0.038 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 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".