Psoriasis and Psoriatic Arthritis Video Project 2010: A Report from the GRAPPA Annual Meeting
Bibliographic record
Abstract
Investigators use several physical examination measures to assess clinical features and severity of psoriasis and psoriatic arthritis (PsA) in clinical trials, clinical registries, and clinical practice; however, no relevant training modules are widely available to teach and standardize the performance of these measures. At a GRAPPA (Group for Research and Assessment of Psoriasis and Psoriatic Arthritis) meeting adjacent to the 2009 International Federation of Psoriasis Associations in Stockholm, members were updated on the development status of online training videos of psoriasis and PsA examination measures. Dermatology assessment modules include the Psoriasis Area and Severity Index, the Static Physician Global Assessment, body surface area, the original and modified Nail Psoriasis Severity Index, the Palmar-Plantar Pustular Psoriasis Area and Severity Index, and the Psoriasis Scalp Severity Index. Rheumatology modules include assessment of tender and swollen joint counts used in the American College of Rheumatology criteria, Disease Activity Score, and other composite arthritis scores; enthesitis assessment used in various enthesitis scoring systems; dactylitis; and spine disease. Each module will include background information for each measure, diagrams and photographs to emphasize teaching points, demonstration video of examination where applicable, and an optional examination at the end. Future plans include evaluating the modules for their influence on interrater and intrarater reliability and development of additional modules.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".