Moving Toward Precision Medicine in Psoriasis and Psoriatic Arthritis
Bibliographic record
Abstract
Current management approaches for the treatment of psoriasis and psoriatic arthritis (PsA) are imprecise and depend largely on clinical assessment. A more precise approach, which takes into account an individual patient's variations in genes, proteins, environment, and lifestyle, is beginning to receive attention with the most advanced progress seen in the treatment of cancer. Herein, the methodological approaches required for this precision medicine approach to be adopted in psoriatic disease, as well as their advantages, are reviewed. In addition, advances that are being made to address areas of unmet need in PsA, notably the use of proteomic approaches, are presented with suggestions that combine genetic and protein data (proteogenomics). Finally, progress that is being made in 2 large-scale, multipartner studies focused on the development of a precision medicine approach to the treatment of skin psoriasis is presented and discussed.
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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 | 0.027 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".