Updates on Recent Advances in the Therapy of Adult Psoriatic Disease
Bibliographic record
Abstract
Psoriatic arthritis (PsA) is a heterogeneous inflammatory disease with various joint and skin manifestations and multiple associated comorbidities. The management of PsA is important not only in controlling disease activity and preventing subsequent damage but also in improving the quality of life and reducing mortality. Over the years, numerous drugs have been introduced into the therapeutic armamentarium of the disease. While non-steroidal anti-inflammatory drugs (NSAIDs) and conventional synthetic disease-modifying anti-rheumatic drugs (DMARDs) have contributed to management, it was not until the advent of biologics (and later on targeted synthetic DMARDs) that therapy was revolutionized, with the achievement of significantly better clinical and radiographic outcomes. Several drugs and treatment approaches are currently being tested in clinical trials at different phases. Despite all the success, there are still various challenges and unmet needs in the field of PsA, reflected by difficult-to-treat disease course, secondary failure of therapy, and lack of consensus on accepted treatment withdrawal protocols, among others. In this mini-review, we have discussed the most recent advances in the therapy of psoriatic disease, with a particular focus on phase III studies completed (or ongoing) since 2020. We also mentioned the challenges and unmet needs in our clinical practice, which we expect current and future research to provide answers to.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".