Abstracts from the 3rd International Severe Asthma Forum (ISAF)
Bibliographic record
Abstract
Respiratory symptoms are common in preschool children. However, which of these wheezers will develop asthma at school age, and what phenotype they will develop remains difficult to predict. Current models such as the asthma prediction index (API) are based on clinical parameters and have only modest predictive accuracy. Expression levels of well replicated asthma genes could potentially form novel biomarkers for asthma prediction. IL1RL1 is an asthma susceptibility gene, and has also been linked to eosinophilia. Therefore, we hypothesized that expression levels of IL1RL1 in the form of soluble IL-1RL1-a measured in serum from wheezing preschool children contribute to the prediction of asthma at school age. Moreover, since IL1RL1 was previously associated with blood eosinophilia, our second aim was to determine whether serum IL-1RL1-a levels predict eosinophilic asthma. Method: We used logistic predictive modeling in a prospective Dutch birth cohort (n = 202 wheezers), and calculated the area under the curve (AUC) of the sensitivity/1-specificity curves of potential models. Results: Neither IL-1RL1-a serum levels at age 2-3 years alone nor its combination with the API had predictive value for doctors' diagnosed asthma at age 6y (IL-1RL1-a alone: AUC = 0.50 [95 CI 0.41-0.59, P = 0.98], API + IL-1RL1-a: AUC = 0.57 [95 CI 0.49-0.66, P = 0.12]). However, IL-1RL1-a serum levels at age 2-3 years correlated with the severity of airway eosinophilia (determined by levels of exhaled fraction of NO, [FeNO]) in children who had developed asthma at age 6y (Pearson's R = -0.24, P = 0.046, N = 59). Logistic predictive modeling of eosinophilic asthma at age 6y (asthma with FeNO 20 ppb) showed that IL-1RL1-a serum levels itself and in combination with the API could predict this eosinophilic subphenotype of asthma (IL-1RL1-a alone: AUC = 0.65 [95 CI 0.52-0.79, P = 0.04], API + IL-1RL1a: AUC = 0.70 [95 CI 0.56-0.84, P = 0.01]). Interestingly, IL-1RL1-a levels had a negative direction of effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".