Prediction of Adult Asthma Risk in Early Childhood Using Novel Adult Asthma Predictive Risk Scores
Bibliographic record
Abstract
This study created a childhood risk score, ASthma PredIctive Risk scorE (ASPIRE), with the goal of using child characteristics to predict current and persistent asthma into young adulthood.The Isle of Wight Birth Cohort (n = 1456) was prospectively assessed at ages 10 (n = 1373; 94.3%), 18 (n = 1313; 90.1%) and 26 (n = 1033; 70.9%) years; 905 (62%) participated in ASPIRE-1, 718 (49%) in ASPIRE-2, 677 (46%) in ASPIRE-3, and 557 (38%) in ASPIRE-4.Four logistic regression models and characteristics within the first 4 years of life (n = 1218, 83.7%) were used to develop asthma predictive scores that were tested for sensitivity, specificity, and area under the curve (AUC) regarding their ability to predict current asthma at 18 and 26 years, and persistent asthma (PA) at 10 and 18 years, and at 10, 18, and 26 years. Models were validated internally and replicated externally up to 18 years using The Manchester Asthma and Allergy Study and The Avon Longitudinal Study of Parents and Children.ASPIRE-1: a 2-factor model (recurrent wheeze [RW] and positive skin prick test [+SPT] at 4 years) for asthma at 18 years (sensitivity: 0.49, specificity: 0.80, AUC: 0.65), and three 3-factor models, ASPIRE-2: (RW, +SPT, and maternal rhinitis) for asthma at 26 years (sensitivity: 0.60, specificity: 0.79, AUC: 0.73), ASPIRE-3: (RW, +SPT, and eczema at 4 years) for PA at 18 years (sensitivity: 0.63, specificity: 0.87, AUC: 0.77), and ASPIRE-4: (RW, +SPT at 4 years and recurrent chest infection at 2 years) for PA at 26 years (sensitivity: 0.68, specificity: 0.87, AUC: 0.80) were able to predict young adult asthma. ASPIRE-1 and ASPIRE-3 scores had good external replication.ASPIRE childhood risk scores predicted asthma persistence into young adulthood, and the 2-factor model performed well compared with the 3-factor models.ASPIRE has used childhood asthma risk scores to predict persistent asthma into adulthood with good predictive value and generally greater specificity, or ability to predict the absence of asthma, than sensitivity, or ability to predict the development of asthma. Further work should evaluate similar characteristics and scores in other cohorts to extend this work for greater generalizability.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Open science | 0.001 | 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".