Long‐term efficacy and safety of infliximab maintenance therapy in patients with plaque‐type psoriasis in real‐world practice
Bibliographic record
Abstract
BACKGROUND: Tumour necrosis factor-α inhibitors, including infliximab (IFX), can improve disease control of plaque-type psoriasis. OBJECTIVES: The Real-World Assessment of Long-Term Infliximab Therapy for Psoriasis (REALITY) study evaluated the efficacy and safety of maintenance IFX therapy in typical clinical settings. METHODS: In this prospective, observational, open-label, multicentre study in patients with plaque-type psoriasis, IFX 5 mg kg was infused at weeks 0, 2 and 6, and every 8 weeks thereafter during a 50-week treatment phase. The primary outcome was ≥ 75% Psoriasis Area and Severity Index (PASI) improvement from baseline to week 50. Patients with ≥ 25% PASI improvement from baseline to the end of the treatment phase were potentially eligible to enter a 48-week extended treatment phase. Response maintenance and other efficacy measures were evaluated. Adverse events (AEs) were collected. RESULTS: In total 660 patients enrolled. Of 521 efficacy-evaluable treatment phase patients (66% male, mean age 46·5 years, mean PASI 18·1), 56·8% achieved PASI 75 at the end of the treatment phase. Response was maintained at week 50 by 64·7% (205/317) of patients who achieved PASI 75 at week 14. During extended treatment, 66·3% (112/169) of patients attained PASI 75 at week 98; response was maintained at week 98 by 71·6% (101/141) of those who achieved PASI 75 at week 50. IFX was generally well tolerated. During treatment, 7·6% (50/659) of patients had serious AEs. During extended treatment, 4·1% (eight of 193) of patients had serious AEs. CONCLUSIONS: PASI 75 response was achieved by 56·8% and 66·3% of patients at weeks 50 and 98, respectively. The AE pattern was consistent with previous reports.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".