American Contact Dermatitis Society Contact Allergy Management Program: An Epidemiologic Tool to Determine Relative Prevalence of Contact Allergens
Bibliographic record
Abstract
BACKGROUND: Data on the prevalence of contact allergy in North America are currently reported by groups of academic contact allergy specialists at select academic centers. Sampling of data from numerous centers across North America, including practices performing more limited patch testing, would provide a broader perspective of contact allergen prevalence in North America. The American Contact Dermatitis Society Contact Allergy Management Program is an ideal tool for collection of epidemiologic data regarding contact allergy prevalence in North America. OBJECTIVE: The aim of the study was to identify the relative prevalence of contact allergy to common contact allergens in North America. METHODS: Mapping of Contact Allergy Management Program (CAMP) data was performed to allow analysis of how frequently searches were performed for various contact allergens. The number of searches performed for specific allergens provides a measure of the relative prevalence of contact allergy to these allergens. RESULTS: The top 35 allergens for the period from November 18, 2012 to November 18, 2013 are reported. Although these data are useful, specific recommendations for minor alterations to CAMP are discussed, which will allow future CAMP data to be stratified and more powerful. CONCLUSIONS: With minor modifications, CAMP can provide a quantum leap in the reporting of contact allergy epidemiologic data in North America.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".