General and Skin-Specific Health-Related Quality of Life in Patients With Atopic Dermatitis Before and During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Few studies have investigated health-related quality of life (HRQoL) in patients with atopic dermatitis (AD) during the COVID-19 pandemic. OBJECTIVES: The objectives of this study were to compare HRQoL in adult AD patients before and during the pandemic and to assess measurement performance of 4 HRQoL measures. METHODS: Between 2018 and 2021, a multicenter, cross-sectional survey was conducted, involving 218 adult AD patients. Health-related quality of life outcomes included the EQ-5D-5L, Skindex-16, Dermatology Life Quality Index (DLQI), and DLQI-Relevant (DLQI-R). Severity was measured using objective SCORing Atopic Dermatitis, Eczema Area and Severity Index, and Investigator Global Assessment. RESULTS: The mean ± SD EQ-5D-5L utility, Skindex-16, DLQI, and DLQI-R scores were 0.82 ± 0.22, 56.84 ± 27.46, 13.44 ± 8.46, and 13.76 ± 8.60, respectively. The patients reported more problems during the pandemic ( P < 0.05) regarding pain/discomfort (odds ratio [OR], 1.78), worrying (OR, 1.89), concerns about persistence/reoccurrence of disease (OR, 1.88), and social relationships (OR, 1.69). The HRQoL outcomes showed strong correlations with each other (range of rs , |0.69| to |0.99|). The Skindex-16, DLQI, and DLQI-R were able to discriminate between severity groups with large (η 2 = 0.20-0.23), whereas the EQ-5D-5L with moderate effect sizes (η 2 = 0.08-0.11). CONCLUSIONS: Atopic dermatitis patients experienced significantly more problems in some areas of HRQoL during the pandemic. The EQ-5D-5L, Skindex-16, DLQI, and DLQI-R demonstrated good convergent and known-group validity and can be suitable instruments for HRQoL assessment in clinical and research settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".