Patient and Caregiver Perspectives on the Relationship between Atopic Dermatitis Symptoms and Mental Health
Bibliographic record
Abstract
Abstract: Background: Atopic Dermatitis (AD) patients have increased likelihood of developing depression and anxiety. The patient and caregiver's perceptions of the correlation of mental health (MH) and AD symptoms are not well understood. Objective: To evaluate patient-reported MH symptoms and their correlation with AD disease severity and understand patient-perceived associations of AD with impacts to their MH. Methods: Adult AD patients (18+ years) or caregivers of AD patients (8–17 years) were recruited to complete a survey about MH and their perception of its relationship with AD. Results: Of 1496 respondents, 954 met inclusion criteria and completed the survey. Respondents were primarily adults (83.3%) with moderate AD (31.4%). In total, 26.0% reported MH symptoms >10 days per month, and most adults (65.5%) scored in the borderline/abnormal range on the Hospital Anxiety and Depression Scale. Most (70.6%) respondents perceived their MH was negatively affected by AD in the past 12 months. AD severity impacted the perception of the relationship between AD and MH; respondents were more likely to believe MH was impacted by AD when they/their child had severe AD. Conclusion: Our study highlights patient and caregiver awareness of the detrimental impact of AD on MH. Addressing MH in AD care settings early in the disease journey may be beneficial.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".