Patients’ Perception of the Impact of Moderate to Severe Atopic Dermatitis on Their Sexual Well-Being: Comparison of Pre- and Posttreatment with Advanced Therapies
Bibliographic record
Abstract
Abstract: Background: Atopic dermatitis (AD) is a chronic inflammatory skin disease that significantly affects patients’ quality of life (QoL). Limited data exists on the effects AD poses on sexual well-being. Objective: To evaluate the impact of moderate-to-severe atopic dermatitis (MtS-AD) on sexual well-being and assess the efficacy of advanced therapies, specifically abrocitinib and dupilumab, in improving sexual health. Methods: In this retrospective study, medical records and responses to an 8-item questionnaire assessing sexual well-being in patients with MtS-AD were reviewed at baseline and after 52 weeks of treatment with abrocitinib or dupilumab. Results: In total, 44 patients were included (mean age: 30.8, 43.2% female). Before treatment, 88.6% reported AD affected their sexual well-being, including feeling unattractive (79.5%), avoiding sexual activity (68.2%), feeling ashamed or embarrassed (68.2%), and experiencing rejection (56.8%). After 52 weeks of treatment with abrocitinib or dupilumab, these negative perceptions and behaviors significantly reduced. Conclusions: MtS-AD negatively impacts patients’ sexual well-being; however, advanced therapies like abrocitinib and dupilumab can significantly improve the sexual lives of affected patients, highlighting the broader benefits of these treatments on QoL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".