Challenges and Opportunities in Psoriatic Disease: An Integrated View of the Future
Bibliographic record
Abstract
Psoriatic disease (PsD), which includes cutaneous psoriasis (PsO) and psoriatic arthritis (PsA), affects 2% of the global population, and results in the development of comorbidities that adversely affect quality of life (QOL) and physical function. Recent advances in the field have allowed for earlier diagnosis of PsD and improved clinical strategies for care, including the use of innovative pathway-specific immune-targeted therapies. Despite these advances, there is no cure for PsD. Ongoing challenges in disease management include the need for adequate treatment response, precision-based care for individual patients, and a better understanding of the interrelationship between the pathogenesis of cutaneous PsO and PsO comorbidities, including PsA. Future progress may arise from integrating clinical disciplines, harnessing artificial intelligence, using molecular dissection to map out the disease pathogenesis of PsA to identify more effective treatment strategies, and exploring the interplay between PsD and comorbidities including cardiovascular disease, obesity, and depression. These developments could lead to personalized treatment approaches and increase the efficacy of therapeutics for PsD, ultimately improving patient outcomes and QOL. This article highlights the presentation of this topic at the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2024 annual meeting.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".