Assessing Quality of Life in Patients With Atopic Dermatitis: A Case–Control Study
Bibliographic record
Abstract
Abstract: Background: Atopic dermatitis (AD) is a chronic, itchy inflammatory disease that significantly affects quality of life (QoL). Assessing this impact is essential for optimal management. Objective: The aim of the study was to evaluate the impact of AD on QoL in affected patients and their families. Methods: We conducted a case–control study at the Monastir Dermatology Department over 4 months (September–December 2022), including 100 patients with the disease and 100 controls. QoL was assessed using Dermatology Life Quality Index (DLQI) (>15 years), Children’s Dermatology Life Quality Index (CDLQI) (5–15 years), IDQOL (<5 years), and Dermatitis Family Impact (DFI) for families. Results: The median age of patients and controls was 13.5 years, with a female predominance (F/H ratio = 1.32). The median onset age was 5 years. QoL was significantly worse in patients with AD and their families. CDLQI and DLQI showed moderate positive correlations with Scoring Atopic Dermatitis Index (SCORAD) ( P = 0.004; r = 0.434 and P = 0.033; r = 0.322), while DFI had a strong correlation ( P < 0.001; r = 0.575), reflecting the family burden. Beyond disease severity, QoL was influenced by pruritus intensity, socioeconomic status, environmental factors, and eczema extent, topography, and appearance. Conclusions: AD significantly impacts patients’ and families’ QoL. Specific QoL assessment scales are essential for optimizing management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".