Clinical and Pathologic Factors Predicting Future Asthma in Wheezing Children. A Longitudinal Study
Bibliographic record
Abstract
Wheeze is a common symptom in infants, but not all wheezers develop asthma. Indeed, up to 50% of wheezing children outgrow their symptoms by school age. How to predict if early wheeze will become asthma is still a matter of vivid debate. In this work, we sought to assess the clinical and pathological factors that might predict the future development of asthma in children. Eighty children (mean age 3.8 ± 1 yr) who underwent a clinically indicated bronchoscopy were followed prospectively for a median of 5 years. At baseline, clinical characteristics with a particular focus on wheezing and its presentation (episodic or multitrigger) were collected, and structural and inflammatory changes were quantified in bronchial biopsies. Follow-up data were available for 74 of the 80 children. Children who presented with multitrigger wheeze were more likely to have asthma at follow-up than those with episodic wheeze (P = 0.04) or without wheeze (P < 0.0001). Children with asthma also had lower birth weights (P = 0.02), a lower prevalence of breastfeeding (P = 0.02), and a trend for increased IgE (P = 0.07) at baseline than those with no asthma. Basement membrane thickness and airway eosinophils at baseline were increased in children who developed asthma at follow-up (P = 0.001 and P = 0.026, respectively). Multivariate analysis showed that among all clinical and pathological factors, multitrigger wheezing, basement membrane thickening, and reduced birth weight were predictive of future asthma development. We conclude that multitrigger wheeze and reduced birth weight are clinical predictors of asthma development. Basement membrane thickening in early childhood is closely associated with asthma development, highlighting the importance of airway remodeling in early life as a risk factor for future asthma.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".