Wheeze trajectories are modifiable through early‐life intervention and predict asthma in adolescence
Bibliographic record
Abstract
BACKGROUND: The objectives of this study were to identify developmental trajectories of wheezing using data-driven methodology, and to examine whether trajectory membership differentially impacts the effectiveness of primary preventive efforts that target modifiable asthma risk factors. METHODS: Secondary analysis of the Canadian Asthma Primary Prevention Study (CAPPS), a multifaceted prenatal intervention among children at high risk of asthma, followed from birth to 15 years. Wheezing trajectories were identified by latent class growth analysis. Predictors, intervention effects, and asthma diagnoses were examined between and within trajectory groups. RESULTS: Among 525 children, 3 wheeze trajectory groups were identified: Low-Progressive (365, 69%), Early-Transient (52, 10%), and Early-Persistent (108, 21%). The study intervention was associated with lower odds of Early-Transient and Early-Persistent wheezing (P < .01). Other predictors of wheeze trajectories included, maternal asthma, maternal education, city of residence, breastfeeding, household pets, infant sex and atopy at 12 months. The odds of an asthma diagnosis were three-fold to six-fold higher in the Early-Persistent vs Low-Progressive group at all follow-up assessments (P = .03), whereas Early-Transient wheezing (limited to the first year) was not associated with asthma. In the Early-Persistent group, the odds of wheezing were lower among intervention than control children (adjusted odds ratio: 0.67; 95% CI: 0.48; 0.93) at 7 years. CONCLUSIONS: Using data-driven methodology, children can be classified into clinically meaningful wheeze trajectory groups that appear to be programmed by modifiable and non-modifiable factors, and are useful for predicting asthma risk. Early-life interventions can alter some wheeze trajectories (ie, Early-Persistent) in infancy and reduce wheezing prevalence in mid-childhood.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".