A Preliminary Report of the Occupation of Patients Evaluated in Patch Test Clinics
Bibliographic record
Abstract
Background: The interplay between the occupational environment and worker's skin can result in contact dermatitis of both irritant and allergic types. Other forms of dermatitis can also be influenced by occupational exposures. Objective: The aim of this study is to compare the occupations and allergens of occupational contact dermatitis cases with nonoccupational contact dermatitis cases. Methods: Diagnostic patch testing with allergens of the North American Contact Dermatitis Group and occupational coding by the National Institute for Occupational Safety and Health methods. Results: Of 2,889 patients referred for evaluation of contact dermatitis, 839 patients (29%) were found to have occupational contact dermatitis. Of the 839 cases deemed occupational, 455 cases (54%) were primarily allergic in nature and 270 cases (32%) were primarily irritant in nature. The remaining 14% were diagnoses other than contact dermatitis, aggravated by work. The occupation most commonly found to have allergic contact dermatitis was nursing. Allergens strongly associated with occupational exposure were thiuram, carbamates, epoxy, and ethylenediamine. Conclusion: Some contact allergens are more commonly associated with occupational contact dermatitis. Nursing and nursing support are occupations most likely to be overrepresented in contact dermatitis clinics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".