Proceedings of the 2017 GRAPPA Collaborative Research Network Meeting
Bibliographic record
Abstract
The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) Collaborative Research Network (CRN) is an endeavor that aims to address gaps in the knowledge of the etiopathogenesis and management of psoriatic disease by best using the large community of experienced investigators who are already collecting rich clinical phenotype data and biologic samples using validated techniques. Exemplar rheumatology and dermatology projects will inform strategies to implement the CRN, while input and funding from government organizations, charities, and industry will shape the CRN. The key immediate priorities to establish the CRN are discussed herein and include (1) strategies for building infrastructure to collect and store biosamples and associated clinical data, (2) best practices for sample collection and storage, (3) approaches to engage the GRAPPA community of investigators and industry to collaborate most effectively on shared priorities, and (4) agreement on a funding strategy. The following 4 CRN candidate flagship research areas were identified: (1) predictors of treatment response in psoriatic arthritis (PsA) and cutaneous psoriasis (PsC) to permit personalized and stratified medicine approaches; (2) predictors of structural damage and disease severity, linking with the existing PsA BioDAM project; (3) predictors of PsC progressing to PsA to enable earlier intervention and possibly halt progression to PsA; and (4) comorbidity prevalence and effect on clinical outcomes in psoriatic disease. The collaboration and momentum provided by a GRAPPA-CRN will offer more than the sum of its individual contributing centers. A CRN will permit high-quality research that can more effectively address questions pertinent to patients, clinicians, scientists, industry, and governments.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.117 | 0.059 |
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".