Adult Vulvar Lichen Sclerosus: Can Experts Agree on the Assessment of Disease Severity?
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to test the severity rating of the signs and architectural changes for interrater reliability among world experts via analysis of lichen sclerosus (LS) photographs. METHODS: A recent Delphi consensus exercise established a list of symptoms, signs, and architectural changes, which experts feel are important to include in a severity scale. Photographs of vulvar LS were manually extracted from patient charts and 50 photographs with a range of severity of signs and architectural changes were chosen. Lichen sclerosus experts were invited to take part in the study and 3 dermatologists and 3 gynecologists were selected for their expertise and geographic variety. Raters assessed the photographs for multiple signs and architectural changes as well as an overall impression of disease severity on a 4-point Likert scale. Intraclass correlation coefficients were calculated. RESULTS: The intraclass correlation coefficients were very poor for individual signs and architectural changes as well as for overall disease severity when analyzed for all 6 raters as well as when analyzed with dermatologists' and gynecologists' responses grouped separately. There were no statistically significant correlations found. CONCLUSIONS: Global experts were unable to agree on any signs, architectural changes, or an overall global impression to assess vulvar LS disease severity based on analysis of vulvar photographs. Standardized descriptions regarding what constitutes mild, moderate, and severe signs and anatomical changes are required before further scale development can occur.
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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 | 0.107 | 0.265 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".