Vulvar Lichen Sclerosus: Outcomes Important to Patients in Assessing Disease Severity
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to determine outcome measures that women with vulvar lichen sclerosus (LS) rate as important in assessing disease severity with the ultimate goal of including these items in a disease severity rating tool. METHODS: An online survey of women older than 18 years with a diagnosis of vulvar LS was performed. The survey was posted in Facebook LS support groups. Participants rated items on a scale from 1 to 5 (not important to include to essential to include) in a disease severity scale. Participants also rated how often they were affected by various symptoms on a scale from 1 to 5 (never to daily). Mean rating of importance and mean rating of frequency for each sign and symptom were calculated. T tests were used to compare patients with biopsy-proven disease with those with a clinical diagnosis of LS. RESULTS: Nine hundred fifty-eight participants completed the survey (86% completion rate). Patients felt that the most important items to assess disease severity were irritation (4.39), fusion of the labia (4.38), soreness (4.37), itch (4.34), change in vulvar skin (4.34), and decrease in quality of life (4.33). The most frequently experienced items by those with LS were irritation (3.92), changes in appearance of vulvar skin (3.92), and discomfort (3.89). There were no differences between patients with biopsy-proven LS versus those diagnosed on clinical examination. CONCLUSIONS: Future LS severity assessment tools will need to include a combination of patient-rated symptoms, clinical rated signs and anatomical changes, and quality of life measures.
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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 | 0.005 | 0.017 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".