Association of Adverse Childhood Experiences with Childhood Atopic Dermatitis in the United States
Bibliographic record
Abstract
Traumatic and stressful events of childhood, known as adverse childhood experiences (ACEs), have been associated with numerous health outcomes. However, little is known about ACEs in atopic dermatitis (AD) patients. We sought to determine the relationship between ACEs and childhood AD. Data were analyzed from the Fragile Families and Child Wellbeing Study, a longitudinal birth cohort study that followed 4898 women and their children born in large US cities. Multivariable weighted logistic regression models adjusting for sociodemographics were constructed to determine the associations of ACEs with AD prevalence at ages 5, 9, and 15 years. Children who experienced 1 ACE (multivariable logistic regression; adjusted odds ratio [aOR], 1.42; 95% confidence interval [CI], 1.08-1.86), 2 ACEs (1.49; 95% CI, 1.10-2.02), or 3 or more ACEs (2.10; 95% CI, 1.52-2.89) had significantly increased odds of AD history compared with children without ACEs at age 5 years. Children who experienced 3 or more ACEs (1.48; 95% CI, 1.09-2.01) had significantly increased odds of AD history compared with children without ACEs at age 9 years. There were no significant associations between ACEs and history of AD at age 15 years. In conclusion, ACE exposures are related to childhood AD across time. Children who experience a greater number of ACEs have higher prevalence of AD.
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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 | 0.000 | 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 | 0.001 |
| 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".