Psoriasis and Psoriatic Arthritis Video Project: An Update from the GRAPPA 2011 Annual Meeting
Bibliographic record
Abstract
Numerous physical examination instruments are used to assess and measure severity of psoriasis and psoriatic arthritis (PsA) in practice and in clinical trials. The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) has developed several online training modules used by GRAPPA members and investigators participating in psoriasis and PsA research. At the 2011 GRAPPA meeting, attendees were updated on the ongoing development of the training modules. Several Internet-based multimedia presentations for psoriasis and PsA assessments have been completed. Available psoriasis modules include the Psoriasis Area and Severity Index (PASI) and Body Surface Area, one 5-point and two 6-point Physician Global Assessments, the original and modified Nail Psoriasis Severity Index, the Palmar-Plantar Pustular Psoriasis Area and Severity Index, the Psoriasis Scalp Severity Index, and the Total Plaque Severity Score. Rheumatology modules that demonstrate evaluation of swollen and tender joints, enthesitis, and dactylitis are now available, and an axial disease evaluation module is near completion. Each video includes the background and rationale for each measure, demonstration videos of select examinations, diagrams, and photographs to emphasize teaching points, and for most dermatology modules, an optional test to assess competence. Preliminary data generated by a pilot study of pre- and post-education PASI scoring by experienced and naive physicians and patient assessors were presented, revealing improved accuracy of scoring after viewing the PASI video. Attendees agreed that additional patient examples with more diverse skin types and psoriasis phenotypes, translation to languages other than English, and further validation studies are needed.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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".