Cutaneous Comorbidities Associated With Atopic Dermatitis in Israel: A Retrospective Real-World Data Analysis
Bibliographic record
Abstract
Background Patients with atopic dermatitis (AD) are susceptible to infectious and inflammatory cutaneous comorbidities. Objective The aim of the study was to describe the prevalence of cutaneous comorbidities associated with AD, including their relationship with AD severity. Methods A retrospective cross-sectional analysis was performed using the Israeli Maccabi Healthcare Services database. Prevalent AD cases on December 31, 2017, were diagnosed with AD at any time since 1998 and had 1 or more recent (2013–2017) AD diagnoses. Dispensed AD treatments within 5 or fewer years served as a surrogate for AD severity. Cutaneous comorbidities in AD cases were compared with non-AD controls matched 1:1 on age, sex, and residential area. Among adults, comorbidities were compared across AD severity using multinomial logistic regression. Results The eligible population included 94,483 patients with mild (57.7%), moderate (36.2%), or severe (6.1%) AD, and 94,483 matched non-AD controls. Skin infections, inflammatory skin conditions, cutaneous manifestations of AD, and sweat gland disorders were more prevalent (P < 0.001) in patients with AD than in controls. Most cutaneous comorbidities that were more prevalent in adult patients with AD were also significantly (P < 0.001) associated with AD severity. Conclusions This study suggests that AD is associated with many infectious and inflammatory cutaneous comorbidities and highlights the relationship between AD severity and comorbidity prevalence.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".