Bibliographic record
Abstract
Aim: This narrative review aims to identify key elements that facilitate the transition from recurrent episodes of wheezing to chronic inflammatory airway disease. Methods: The article presents the results of numerous studies that identify the influence of genetic and environmental factors on the development of asthma in children. Whole-genome data analysis revealed novel genetic loci associated with various asthma phenotypes. Additionally, the study underscored the significance of environmental factors, such as air pollution and microbial colonization, in the disease's onset. Results: The results provided a foundation for developing new prevention and treatment strategies for childhood asthma, emphasizing a personalized approach that considers each patient's unique genetic and environmental profile. The main findings indicate that up to 50% of children under 6 years old experience wheezing episodes, but only 30% of these children develop asthma. Data analysis demonstrated that both genetic and environmental factors significantly influence asthma development in children with preschool wheezing. Genetic research has identified several genes associated with early-onset asthma, including CDHR3, IL33, and genes at the 17q12-21 locus. Surrounding conditions such as viral infections, allergens, tobacco smoke, and the microbiome also play a substantial role in asthma development. Conclusions: Understanding the relationship between hereditary and environmental influences in the advancement from preschool wheeze to asthma is crucial for developing effective prophylactic and treatment strategies. The study of factors influencing the development of asthma in children is important for understanding the mechanisms of disease formation and developing effective methods of prevention and treatment. Special attention is paid to the interaction of genetic and external factors influencing the early stages of pathogenesis.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".