Psychometric validation of the generalized pustular psoriasis physician global assessment (<scp>GPPGA</scp>) and generalized pustular psoriasis area and severity index (<scp>GPPASI</scp>)
Bibliographic record
Abstract
BACKGROUND: Generalized pustular psoriasis (GPP) is a rare and life-threatening skin disease often accompanied by systemic inflammation. There are currently no standardized or validated GPP-specific measures for assessing severity. OBJECTIVE: To evaluate the reliability, validity and responder definitions of the Generalized Pustular Psoriasis Physician Global Assessment (GPPGA) and Generalized Pustular Psoriasis Area and Severity Index (GPPASI). METHODS: The GPPGA and GPPASI were validated using outcome data from Week 1 of the Effisayil™ 1 study. The psychometric analyses performed included confirmatory factor analysis, item-to-item/item-to-total correlations, internal consistency reliability, test-retest reliability, convergent validity, known-groups validity, responsiveness analysis and responder definition analysis. RESULTS: Using data from this patient cohort (N = 53), confirmatory factor analysis demonstrated unidimensionality of the GPPGA total score (root mean square error of approximation <0.08), and GPPGA item-to-item and item-to-total correlations ranged from 0.58 to 0.90. The GPPGA total score, pustulation subscore and GPPASI total score all demonstrated good test-retest reliability (intraclass correlation coefficient: 0.70, 0.91 and 0.95 respectively), and good evidence of convergent validity. In anchor-based analyses, all three scores were able to detect changes in symptom and disease severity over time; reductions of -1.4, -2.2 and - 12.0 were suggested as clinically meaningful improvement thresholds for the GPPGA total score, GPPGA pustulation subscore and GPPASI total score respectively. Anchor-based analyses also supported the GPPASI 50 as a clinically meaningful threshold for improvement. CONCLUSIONS: Overall, our findings indicate that the GPPGA and GPPASI are valid, reliable and responsive measures for the assessment of GPP disease severity, and support their use in informing clinical endpoints in trials in GPP.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".