Allergic Contact Dermatitis and Concomitant Dermatologic Diseases: A Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Allergic contact dermatitis (ACD) can exist in the setting of other dermatologic conditions. It is known that the treatment of these conditions can cause ACD, increasing both diagnostic and treatment difficulty. OBJECTIVE: The aim of this study was to determine the frequency of common dermatologic conditions in the setting of ACD and in specific patient populations. METHODS: A retrospective database study was completed using Truven Health to collect information on patch-tested ACD patients. Demographics and diagnostic information were retrieved. Of those with ACD, the presence of 15 dermatologic diagnoses was investigated. Subanalyses were conducted for each condition, including International Classification of Diseases, 10th Revision code specificity, demographics, and diagnostic information. RESULTS: A total of 6380 patients (76.83% female) were given a diagnosis of ACD via patch testing. Of those with concomitant disease, those most common include atopic dermatitis (23.98%), urticaria (16.69%), and acne (11.51%). Eight of the concomitant conditions were found to have statistical significance in comparing the average age of ACD diagnosis with the selected diagnoses (α = 0.05). CONCLUSIONS: Common dermatologic diseases can exist concomitantly with ACD, many of which can be treated by compounds that precipitate or worsen preexisting ACD. The average age of the diagnosis varies from concomitant diagnoses, which can contribute to difficulty in ACD diagnosis and treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".