Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD) is an inflammatory skin disorder that is exceedingly challenging to treat. A prominent feature of AD is chronic pruritus. Early evidence suggested that pruritus in AD was partially due to mast cell release of histamines. Conversely, recent studies do not validate the role of histamine in the pathogenesis of pruritus. Conventional management continues to include the wide use of antihistamines to treat the persistent itch, however, there is an urgent need for therapy which will reduce the severity of pruritus for these patients. OBJECTIVE: To review the evidence in the literature for the use of antihistamines in the treatment of atopic dermatitis. METHODS: A MEDLINE search (1966-2002) was performed to obtain studies examining the use of antihistamines in the treatment of atopic dermatitis. Search terms included: atopic dermatitis; eczema; antihistamines; azatadine; brompheniramine; cetirizine; chlorpheniramine; clemastine; cyclizine; cyproheptadine; desloratadine; diphenhydramine; fexofenadine; hydroxyzine; loratadine; meclizine; promethazine; trimeprazine. Further references were gathered from these publications. RESULTS: Historically, antihistamines have been used in the treatment of AD. However, this review shows that the evidence for its use is inconclusive. At present, several antihistamines continue to provide relief of pruritus by central sedation, and they can also be used therapeutically for concomitant allergic conditions associated with AD. More clinical trials examining the therapeutic efficacy of antihistamines, especially with the newer nonsedating antihistamines, are necessary to elucidate their role in the treatment of AD. CONCLUSION: Dermatologists require additional evidence regarding the efficacy of antihistamines and their mechanism of action in the treatment of AD to enhance patient care.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".