Clinical severity of atopic dermatitis is associated with dental caries risk in 3-year old children
Bibliographic record
Abstract
Background: Infants with atopic dermatitis (AD) are reported to be at higher risk of early childhood caries (ECC) at 3-years, but the clinical validity of the reported link remains unknown. We investigated if clinical severity of AD in young children is associated with increased ECC risk at 3-years. Methods: In Growing Up in Singapore Towards healthy Outcomes (GUSTO) mother-offspring cohort, AD was diagnosed by trained physicians using Hanifin and Rajka criteria at 18-month and 3-year clinic visits (n=837). Of the children diagnosed with AD, disease severity was assessed using SCORAD (SCORing Atopic Dermatitis) index and categorized into moderate-to-severe AD (SCORAD≥25), and mild AD (SCORAD<25), with children without AD (non-AD) as a reference group. Oral examinations for ECC detection was performed by calibrated dentists in 656 children at age 3-years. Negative binomial regression was used to calculate the adjusted incidence risk ratio (aIRR; adjusted for socio-demographic factors and prenatal tobacco smoke exposure). Results: Atopic dermatitis was diagnosed in 7.3% (61/837) children; amongst which 23% had moderate-to-severe AD and 77% had mild AD. ECC was observed in 85.7%, 36.8% and 42.8% of the children in moderate-to-severe, mild and non-AD groups, respectively. Children with moderate-to-severe AD were at higher risk of ECC (aIRR 2.30 [95% confidence interval (CI) 1.04-5.06]; p=0.03) at 3 years compared to non-AD, while no association was seen between mild AD and ECC. Conclusions: Children with moderate-to-severe atopic dermatitis were at higher risk of ECC compared to those without AD and may benefit from early dental referral.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".