Correlation of photographic images from the Leeds revised acne grading system with a six‐category global acne severity scale
Bibliographic record
Abstract
BACKGROUND: Few global acne grading systems include individualized assessments of chest and back as well as face. The Leeds revised acne grading system is based on photographic images while the Comprehensive Acne Severity Scale (CASS) is based on textual descriptions. There exists an unmet need for a global scale based on both text and photos. PURPOSE: Our objective was to evaluate the correspondence of severity grades from descriptive text (CASS grades) for each Leeds image. METHODS: Twenty-three dermatologists independently graded 56 photos of face, chest and back of varying acne severity using CASS. Photographs were randomly presented from the The Leeds revised acne grading system (n = 31) and from acne patients of the corresponding author (n = 25). For each Leeds photo, rater responses for CASS grades were transformed into median, coefficient of variation and percentiles. RESULTS: High rater agreement (≥75%) was observed for Leeds facial inflammatory 2 (CASS 3), 4 (CASS 4), 6 (CASS 4), 9-12 (all CASS 5); Leeds facial comedonal A (CASS 2); Leeds chest 7 and 8 (both CASS 5); and Leeds back 7 and 8 (both CASS 5). Lowest coefficients of variance were observed in Leeds facial inflammatory 4, 9, 10, 11; Leeds facial comedonal A; Leeds chest 7 and 8; and Leeds back 8. Conclusions Leeds photos, by inadequately portraying facial acne grades 1 (almost clear) and 2 (mild) and back and chest grades 1-4 (almost clear to severe), cannot accurately represent the spectrum of severity in a six-category global acne scale. Accordingly, there is a current need for images that correspond to a categorical acne scale.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".