Diagnosis and Prevalence of Lanolin Allergy
Bibliographic record
Abstract
BACKGROUND: Current evaluation of suspected allergic contact dermatitis to lanolin includes patch testing to lanolin alcohol (30% in petrolatum). Using this method, the prevalence of lanolin allergy is low (1.8%-2.5%). OBJECTIVE: The objective of this study was to determine whether patch testing to a single lanolin derivative results in underdiagnosis compared with patch testing to 12 lanolin derivatives. METHODS: Patients were prospectively patch tested to (1) lanolin alcohol (30% in petrolatum) in our standard allergen series; (2) Amerchol L101 (50% in petrolatum) in our cosmetic series; and (3) 10 lanolin derivatives (using concentrations and vehicles recommended in the literature) in a supplemental series. RESULTS: Of 286 patients, the overall prevalence of positive reactions to lanolin in at least 1 of the 3 patch test series was 6.29% (95% confidence interval [CI], 3.48%-9.11%) (n = 18). The prevalence rates of lanolin allergy using the standard, cosmetic, and supplemental series were 1.05% (95% CI, 0%-2.23%), 3.85% (95% CI, 1.62%-6.07%), and 3.85% (95% CI, 1.62%-6.07%), respectively. Amerchol L101 was associated with increased reaction rates compared with the standard (odds ratio, 3.81; P = 0.007) and supplemental (odds ratio, 8.85; P < 0.001) series, whereas reaction rates were similar for the standard and supplemental series (P = 0.78). CONCLUSIONS: Amerchol L101 and patients' own products should be added to a standard patch testing allergen series to adequately identify lanolin allergy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".