Access, Effectiveness, Safety, and Survival of Secukinumab in Patients With Axial Spondyloarthritis and Axial Psoriatic Arthritis: A Real-World Study in Argentina
Bibliographic record
Abstract
Objective To describe the access, effectiveness, survival, and adverse events (AEs) of secukinumab (SEC) in patients with axial spondyloarthritis (axSpA) and axial psoriatic arthritis (axPsA). Methods In a multicenter, observational, retrospective cohort study, patients ≥ 18 years with axSpA or axPsA who had received ≥ 1 dose of SEC were included. The number of days between the request for the drug and the first application was calculated. Effectiveness was defined as Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) < 4 at 6 months. Drug survival was analyzed using Kaplan-Meier curves and Cox regression analysis. Results One hundred seventeen patients were included: 72 (61.5%) with axPsA and 45 (38.5%) with axSpA. Those having public health insurance presented a longer delay in receiving SEC (median 90 days [IQR 60-150]) vs those using the social security system (P = 0.01) and those with private health coverage (P = 0.009). Effectiveness of SEC after 6 months was achieved in 72/117 patients (61.5%): 44/72 patients with axial PsA (61.5%) and 28/45 patients with axSpA (62.2%; P = 0.91). The median SEC survival was 48 months (95% CI 32-63). The only factor associated with reduced survival was SEC as third-line treatment or higher (hazard ratio 3.43, 95% CI 1.11-11.10; P = 0.04). The incidence of AEs was 7.9 events/100 patients/year (95% CI 5-12). Conclusion The delay in receiving SEC was longer in patients with public health insurance. Patients using SEC as third-line or higher therapy had 3.4 times less survival. AEs were mild and no AEs of interest were observed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".