GRAPPA 2023 Basic Science Workshop: What to Expect From Animal Models for Psoriatic Arthritis and Psoriasis
Bibliographic record
Abstract
Animal models help to drive research into psoriasis and psoriatic arthritis (PsA), particularly when studies in humans are not feasible. There are no animal models that perfectly mimic psoriatic disease (PsD) and so the pros and cons of each existing model must be considered for appropriate experimental design. Roughly, the existing animal models for PsD can be divided into 4 categories: (1) spontaneous models, (2) transgenic models, (3) inducible models, and (4) xenotransplantation models. Animal models in PsD are extremely important for dissecting and understanding molecular mechanisms of the disease process and for developing novel drugs. Animal models remain highly valuable for research in PsD in 2 scenarios. The first scenario is when complex interventions or analyses are required that are not feasible in humans due to technical, safety, or economic reasons. The second is when well-controlled study environments are required, such as dietary modifications, that would be challenging in humans. This topic was presented as part of the basic science workshops during the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2023 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 | 0.014 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".