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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".