Patch Testing for Cheilitis: A 10-Year Series
Bibliographic record
Abstract
BACKGROUND: The etiology of cheilitis includes endogenous, irritant, and allergic dermatitis; lichenoid and granulomatous disorders; infection; trauma; and actinic damage. Patch testing is indicated for refractory cases (other than actinic cheilitis). OBJECTIVE: The aim of the study was to review demographics and allergens in patients patch tested for cheilitis at 2 sites in Sydney, Australia. METHODS: Records for patients patch tested for a 10-year period from 2007 to 2017 were reviewed. Baseline characteristics and patch test results were compared for patients with and without cheilitis. CONCLUSIONS: There were 1584 patients including 91 with cheilitis. Patients with cheilitis were more likely to be female, younger, and atopic and have concurrent eyelid involvement than those presenting with other dermatoses. Seventeen percent of patients with cheilitis had a post-patch test diagnosis of allergic contact cheilitis, and the most frequent relevant reactions were to patients' own products, fragrances, and sunscreens. Those with cheilitis had more positive reactions to sunscreens, especially benzophenones, compared with those without cheilitis (P < 0.001). This is an important finding in Australia where high rates of melanoma and nonmelanoma skin cancer necessitate promotion of strict sun protection 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".