A study examining inter- and intrarater reliability of three scales for measuring severity of psoriasis: Psoriasis Area and Severity Index, Physician's Global Assessment and Lattice System Physician's Global Assessment
Bibliographic record
Abstract
BACKGROUND: There is a lack of consensus as to the best way of monitoring psoriasis severity in clinical trials. The Psoriasis Area and Severity Index (PASI) is the most frequently used system and the Physician's Global Assessment (PGA) is also often used. However, both instruments have some drawbacks and neither has been fully evaluated in terms of 'validity' and 'reliability' as a psoriasis rating scale. The Lattice System Physician's Global Assessment (LS-PGA) scale has recently been developed to address some disadvantages of the PASI and PGA. OBJECTIVES: To evaluate the inter-rater and intrarater reliability of the PASI, PGA and LS-PGA. METHODS: On the day before the study, 14 dermatologists (raters), with varied experience of assessing psoriasis, received detailed training (2.5 h) on use of the scales. On the study day, each rater evaluated 16 adults with chronic plaque psoriasis in the morning and again in the afternoon. Raters were randomly assigned to assess subjects using the scales in a specific sequence, either PGA, LS-PGA, PASI or PGA, PASI, LS-PGA. Each rater used one sequence in the morning and the other in the afternoon. The primary endpoint was the inter-rater and intrarater reliability as determined by intraclass correlation coefficients (ICCs). RESULTS: All three scales demonstrated 'substantial' (a priori defined as ICC > 80%) intrarater reliability. The inter-rater reliability for each of the PASI and LS-PGA was also 'substantial' and for the PGA was 'moderate' (ICC 75%). CONCLUSIONS: Each one of the three scales provided reproducible psoriasis severity assessments. In terms of both intrarater and inter-rater reliability values, the three scales can be ranked from highest to lowest as follows: PASI, LS-PGA and PGA.
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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 | 0.040 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".