Evaluation and Management of Patch Test-Negative Patients With Generalized Dermatitis
Bibliographic record
Abstract
BACKGROUND: Patients with generalized dermatitis are common in dermatology practices. Allergic contact dermatitis is often suspected, and patients frequently undergo patch testing. When the patch testing result is negative, further evaluation and management of these patients are challenging. OBJECTIVE: The purpose of this study was to survey members of the American Contact Dermatitis Society regarding the evaluation and management of patch test-negative patients with generalized dermatitis. RESULTS: Generalized dermatitis was the most common term identified for patch test-negative patients with diffuse dermatitis. After having negative expanded patch testing results, most physicians proceeded with additional testing including skin biopsy, complete blood cell count with differential, and liver and renal function tests. The most commonly used systemic treatment is prednisone, followed by methotrexate. Narrow-band ultraviolet B (UVB) is the most commonly used light source. Antihistamines are frequently prescribed. Food allergy is not felt to be causative. This cohort of patients experiences significant impairment in quality of life, stress on personal relationships, and time off work. CONCLUSIONS: The management of patch test-negative patients with generalized dermatitis is challenging. This study provides insight into management of these complex patients. It also demonstrates practice gaps in the management of these patients, indicating a need for further studies to direct the evaluation and management of this patient population.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".