Predictors for wheezing phenotypes in the first decade of life
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: This study examined prenatal, perinatal and early childhood predictors of wheezing phenotypes in the first decade of life. METHODS: Information on current wheezing, was collected prospectively from five surveys conducted every 2 years over the first decade of life. Five wheezing phenotypes were defined: non-wheezers, preschool, primary-school, intermittent and persistent wheezers. Logistic regression with adjustment for survey design was used to determine the predictors of wheezing phenotypes. RESULTS: Data on 2711 children were used in the analysis. Early respiratory infection, the child's allergy and parental asthma were significant risk factors for preschool, intermittent and persistent wheeze. The child's allergy and parental asthma had stronger associations with persistent wheeze than with preschool wheeze. Breastfeeding was a significant predictor of both preschool and intermittent wheezing. Daycare attendance was a risk factor for preschool wheeze but a protective factor for primary-school wheezing. Crowding at home was a protective factor for both preschool and primary-school wheeze. Parental smoking was a significant factor for preschool wheeze. CONCLUSION: This study identified different predictors for each wheezing phenotype with some degree of overlap. The observed differential effects for these conditions raises the possibility that there are different aetiologies for asthma among children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".