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Record W2037641643 · doi:10.1097/der.0000000000000072

Complementary and Alternative Medicines and Childhood Eczema: A US Population-Based Study

2014· article· en· W2037641643 on OpenAlexvenueno aff
Jonathan I. Silverberg, Mary Lee‐Wong, Nanette B. Silverberg

Bibliographic record

VenueDermatitis · 2014
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDermatologyTraditional medicineFamily medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.298
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2014
Admission routes1
Has abstractyes

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