The Static Physician's Global Assessment of Genitalia: A Clinical Outcome Measure for the Severity of Genital Psoriasis.
Bibliographic record
Abstract
Introduction: Genital psoriasis is a common but frequently overlooked manifestation of psoriasis with a considerable impact on patients' quality of life. Currently no validated clinical trial outcome measures exist to assess genital psoriasis severity that meet regulatory agency requirements. Methods: This study describes the development of the static Physician's Global Assessment of Genitalia (sPGA-G) scale, a clinical outcome measure for the assessment of genital psoriasis severity that accounts for the erythematous clinical presentation of genital psoriasis. The reliability of the sPGA-G was evaluated using scores collected from clinician assessments of photographs of genital psoriasis cases. Scores were collected from 10 academic and clinical experts in genital psoriasis and 95 clinician assessors who participated in either in-person (n=28) or online (n=67) sPGA-G training modules. Results: The sPGA-G had a high inter-rater reliability (IRR, measured by Kendall's W) for expert raters (W=0.856, P less than 0.0001), in-person assessors (W=0.822, P less than 0.0001), and online assessors (W=0.678, P less than 0.0001). IRR was also high for all clinical assessors combined, (W=0.714, P less than 0.0001). Discussion: This study demonstrates that the sPGA-G is an intuitive and reliable clinical outcome measure that specifically measures the severity of genital psoriasis. J Drugs Dermatol. 2017;16(8):793-799. .
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".