Patch-Testing North American Lip Dermatitis Patients: Data from the North American Contact Dermatitis Group, 2001 to 2004
Bibliographic record
Abstract
BACKGROUND: The most common differential diagnoses for patients presenting with lip dermatitis or inflammation include atopic, allergic, and irritant contact dermatitis. Patch testing can be performed to identify the allergic contact conditions. OBJECTIVE: To report North American Contact Dermatitis Group (NACDG) patch-test results of patients who presented for patch testing with only lip involvement from 2001 to 2004. Patient characteristics, allergen frequencies, relevance, final diagnoses, and relevant allergic sources not in the NACDG screening series were evaluated. METHODS: The NACDG 2001-2004 database was used to select patients presenting with only lip involvement. RESULTS: Of 10,061 patients tested, 2% (n = 196) had lips as the sole involved site. Most (84.2%) were women. After patch testing, 38.3% (n = 75) were diagnosed with allergic contact cheilitis. Fragrance mix, Myroxilon pereirae, and nickel were the most common relevant allergens. Of 75 patients, 27 (36%) had relevant positive patch-test reactions to items not on the NACDG series; lipstick and cosmetics were the predominant sources. CONCLUSIONS: Patch testing is valuable in the evaluation and identification of contact allergy in patients referred for lip dermatitis. The use of supplementary allergens based on history and exposure is important in the identification of additional relevant allergens. Over a third of patients with contact allergy had other factors, such as irritant dermatitis, considered relevant to their condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".