Epidemiology and associated factors of atopic dermatitis in Malagasy children
Bibliographic record
Abstract
BACKGROUND: Little is known about the epidemiology and associated factors of childhood AD in the markedly different, low-income, tropical environment like Madagascar. METHODS: We aim to assess the epidemiology and associated factors of AD in individuals fewer than 15 years of age in Antananarivo Madagascar. It was a retrospective and descriptive study over a period of 7 years (2010 to 2016) in children 6 months to 14 years in the Department of Dermatology, Joseph Raseta Befelatanana Antananarivo Madagascar. The diagnosis of AD was based on clinical data. RESULTS: The prevalence of AD was 5.6% in children aged 6 months to 14 years. The details of 151 cases of atopic dermatitis were analyzed. The mean age of patients was 4 years. There was a female preponderance (sex ratio: 0.7). A family history of AD was noted in 56 cases (37%). No association between breast-feeding and AD was found. The age of onset of AD was before the age of 3 months in 7.5% and between 6 months to 5 years in 70%. Children born in March (dry season) had the highest risk of AD. Consultations for AD increased during the winter (from July to October; p = 0.005). However, the prevalence of AD was similar in urban and rural areas. CONCLUSION: Weather may have an impact on the prevalence of atopic dermatitis in Madagascar. No significant correlation was found between the duration of breastfeeding and AD, as well as urbanization.
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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 | 0.001 |
| 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 | 0.000 |
| 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".