Association of previous treatment with anti-tumour necrosis factor inhibitors with the effectiveness of secukinumab in the treatment of psoriatic arthritis: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: We sought to systematically investigate the effectiveness of secukinumab in psoriatic arthritis (PsA) patients who previously received TNFs inhibitor (TNFi) treatment and those who were TNFi naïve. METHODS: Databases (PubMed, EMBase and Cochrane library) and ClinicalTrials.gov were searched from inception to 22 May 2020 for randomized control trails and observational studies of secukinumab, with or without a history of previous anti-TNFi treatment, in PsA. Effectiveness data were extracted and combined using a random-effects meta-analysis. The ACR20 and ACR50 (20% and 50% improvement in American College of Rheumatology response criteria) responses were the endpoints. RESULTS: Six randomized controlled trials that reported the effectiveness of secukinumab by previous anti-TNFi treatment were included. Among patients exposed to a prior anti-TNFi treatment (n = 738), 33.7% (249/738) of patients achieved an ACR20 response. In contrast, in the anti-TNFi-naïve group (n = 1754), 49.8% (873/1754) of patients achieved an ACR20 response. Prior treatment with anti-TNFi was significantly associated with a poorer response to secukinumab compared with the anti-TNFi-naïve group with an effect size of 2.09 (95% CI: 1.69, 2.58). CONCLUSION: Some patients benefit from switching from TNFi to secukinumab, but previous anti-TNFi treatment is associated with poorer effectiveness of secukinumab.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".