Comprehensive Assessment of the Psoriasis Patient (CAPP): A Report from the GRAPPA 2015 Annual Meeting
Bibliographic record
Abstract
Outcome measures for psoriasis severity are complex because of the heterogeneous presentation of the disease. At the 2015 annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA), members introduced the Comprehensive Assessment of the Psoriasis Patient (CAPP), a novel disease severity measure to more accurately assess the full burden of plaque psoriasis and subtypes, including inverse, scalp, nail, palmoplantar, and genital psoriasis. The CAPP is based on a 5-point physician's global assessment for 7 psoriasis phenotypes and incorporates visual analog scale-based, patient-derived, patient-reported outcomes. By quantifying disease effects of plaque psoriasis, 6 other psoriasis subtypes, as well as quality of life and daily function, the CAPP survey identifies a subset of psoriasis patients with moderate to severe psoriasis that would not be considered moderate to severe when assessed by the Psoriasis Area and Severity Index. The current version of CAPP is focused entirely on psoriasis. Feedback from our industry colleagues and collaborators has suggested that a psoriatic arthritis (PsA) measure may be important to include in the CAPP. At the 2015 GRAPPA meeting, we administered a survey to 106 GRAPPA members to determine whether a PsA measure should be included. A majority (74%) of respondents across all professions agreed that the CAPP should include a measure of PsA. Although responses varied widely on how PsA should be measured, a majority of the respondents reported that presence of PsA in both peripheral and axial joint assessment was important.
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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 | 0.030 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".