Contact Dermatitis in the United States: A Population-Based Study on Patient Visit Characteristics and Treatment Prescription Patterns
Bibliographic record
Abstract
Abstract: Background: Contact dermatitis (CD) affects ∼15% of the general population over a lifetime. However, there is a lack of epidemiological studies on treatment patterns for CD. Objective: We aim to analyze the patient characteristics and prescribing patterns among dermatologists and general practitioners (GPs) (internal medicine [IM] and family medicine [FM]) for CD in the United States. Methods: We conducted a population-based study using the National Ambulatory Medical Care Survey. Results: We identified 178,017,680 weighted patient visits for CD from 2001 to 2016. Dermatologists saw more white and non-Hispanic patients than GPs. GPs were less likely to prescribe ultrahigh potency topical corticosteroids (FM OR 0.27; P < 0.001, IM OR 0.41; P < 0.001) and more likely to prescribe oral antihistamines (FM OR 3.71; P < 0.001, IM OR 3.56; P < 0.001), oral corticosteroids (FM OR 5.35; P < 0.001, IM OR 6.87; P < 0.001), and injectable corticosteroids (FM OR 3.42; P = 0.006, IM OR 5.68; P < 0.001) than dermatologists. Conclusions: Across CD visits, GPs were less likely than dermatologists to prescribe ultrahigh potency topical corticosteroids and more likely than dermatologists to prescribe oral antihistamines and systemic corticosteroid therapy.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".