Complementary and Alternative Medicines and Childhood Eczema: A US Population-Based Study
Bibliographic record
Abstract
The prevalence of complementary and alternative medicine (CAM) use in US children with eczema is unknown. Furthermore, it is unknown whether CAM use in the United States is associated with higher eczema prevalence. We sought to determine the eczema prevalence in association with CAM usage. We analyzed data from the 2007 National Health Interview Survey that included a nationally representative sample of 9417 children ages 0 to 17 years. Overall, 46.9% (95% confidence interval, 45.6%-48.2%) of children in the United States used 1 or more CAM, of which 0.99% (0.28%-1.71%) used CAM specifically to treat their eczema, including herbal therapy (0.46%), vitamins (0.33%), Ayurveda (0.28%), naturopathy (0.24%), homeopathy (0.20%), and traditional healing (0.12%). Several CAMs used for other purposes were associated with increased eczema prevalence, including herbal therapy (survey logistic regression; adjusted odds ratio [95% confidence interval], 2.07 [1.40-3.06]), vitamins (1.45 [1.21-1.74]), homeopathic therapy (2.94 [1.43-6.00]), movement techniques (3.66 [1.62-8.30]), and diet (2.24 [1.10-4.58]), particularly vegan diet (2.53 [1.17-5.51]). In conclusion, multiple CAMs are commonly used for the treatment of eczema in US children. However, some CAMs may actually be harmful to the skin and be associated with higher eczema prevalence in the United States.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".