Basic Science Session 1. Biomarkers for Psoriatic Arthritis Treatment Response and Joint Damage Progression: An Update on 2 Industry-GRAPPA Projects
Bibliographic record
Abstract
The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) has identified several priority areas for biomarker development, including biomarkers to predict at baseline which patients may progress to develop joint damage and whether a patient will respond to a specific targeted therapy. Two industry-GRAPPA projects were initiated in 2020 on these biomarker research areas: (1) the Pfizer-GRAPPA project, focused on biomarkers of treatment response to tofacitinib in the Oral Psoriatic Arthritis TriaL program; and (2) the Lilly-GRAPPA project, focused on biomarkers of damage in the ixekizumab SPIRIT-P1 randomized controlled trial. Preliminary results from these 2 projects were presented by the GRAPPA team, with both studies showing promising initial results. Data from these studies will be published when the studies have been completed. Large-scale validation studies are required and are under discussion.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.029 |
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".