Neonatal Filaggrin Genetic Screening and Counseling to Prevent Atopic Dermatitis in High-Risk Infants
Bibliographic record
Abstract
Abstract: Background: Mutations in filaggrin (FLG), the gene that codes for the skin barrier protein, have been shown to be associated with atopic dermatitis (AD). Objective: The objectives of this study were to determine the effects of genetic counseling and parental education on infants at a high risk of AD. Methods: We enrolled 7521 newborns in Taiwan from January 1, 2016, to March 30, 2020, and all of them received genetic testing encompassing 20 known FLG mutations. The genetic counseling and AD prevention and care team consisted of pediatricians, dermatologists, social workers, and genetic counselors. The counseling was arranged for at least 30 minutes within 45 days after delivery. Results: A total of 2963 high-risk infants (39.4%) were identified. Homozygous c.1432C>T was the most commonly identified mutation. A total of 418 neonates' parents were stratified into counseling and noncounseling groups, where the effect of parental education was evaluated. The genetically stratified parental education program was effective in preventing AD development by 63.3% in high-risk infants before 12 months of life ( P < 0.0001). Conclusion: Genetic stratification and parental education are effective in preventing the development of AD in high-risk infants before 12 months of life.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".