The Association between Fatigue and Adult Atopic Dermatitis: A Cross-Sectional Study
Bibliographic record
Abstract
Background: There is currently limited insight into the broader impact of atopic dermatitis (AD) on mental health. Although studies indicate that AD patients may experience fatigue, no study has so far examined fatigue in more granular detail, for example, occurrence of general fatigue, physical fatigue, reduced activity, reduced motivation, and mental fatigue, or correlated fatigue measures with AD severity and symptoms intensity. Objectives: To examine fatigue subtypes and their prevalence in adults with AD, as well as their possible association with AD severity. Methods: A survey was conducted in adults with AD who had been managed in a hospital setting. The Patient-Oriented SCORing Atopic Dermatitis was used to determine AD severity. Patient reported outcomes, including multidimensional fatigue inventory, were included. Results: Data from 2729 adults with AD were analyzed. The total and individual fatigue scores increased consistently with lower socioeconomic scores, higher AD severity, Dermatology Life Quality Index, itch, pain, and sleep scores. Increased fatigue scores were associated with AD severity in adjusted analyses. Conclusions: Among adults with AD, fatigue scores increased with disease severity as well as intensity of AD symptoms. Fatigue is a hitherto underappreciated symptom of AD that clinicians should be cognizant about.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".