Infliximab in Psoriatic Arthritis
Bibliographic record
Abstract
Tumor necrosis factor-α (TNF-α) plays a central role in the pathogenesis of psoriasis and psoriatic arthritis (PsA). Recently, 2 expert committees provided evidence-based recommendations for the treatment of PsA. Anti-TNF-α drugs are indicated in all forms of PsA resistant to traditional therapeutic approaches. Anti-TNF-α infliximab (IFX) is an immunoglobulin G-1 antibody that binds to soluble and cell membrane-bound TNF-α with its inactivation. Confirming previous open-label studies, 2 randomized, placebo-controlled clinical trials, IMPACT and IMPACT2, have provided evidence of the efficacy and safety of IFX in the treatment of PsA as evaluated by American College of Rheumatology (ACR)20, ACR50, and ACR70 response rates. At Week 16, a significant proportion of 51 IFX-treated patients achieved ACR20, ACR50, and ACR70 responses compared to the 51 patients of the placebo arm in the IMPACT trial. In the IMPACT2 study, 58% of the IFX-treated patients and 11% of placebo patients achieved an ACR20 response (p < 0.001), and 41% and 27% of patients in the IFX group were ACR50 and ACR70 responders, compared with 4% and 2% of placebo patients, respectively. Of note, a significant inhibition of radiographic disease progression was observed in the IFX-treated group. In both trials, psoriasis improvement was recorded in more than 80% of patients receiving IFX, with a significant difference compared to placebo group. IFX was well tolerated, with no difference compared to placebo in terms of adverse events. The extension phase of these studies showed the sustained efficacy and safety of the drug.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".