Lower respiratory infections in early life are linked to later asthma
Bibliographic record
Abstract
Asthma is the most prevalent chronic respiratory disease worldwide.1 While much progress has been made to understand the determinants of asthma, why a specific individual develops asthma is not entirely clear. Within this discussion, it should be noted that asthma is not a homogenous disease; that is to say, there are many (endo)types of asthma.2 In children, the most common pattern is a T2 exaggerated immune response with eosinophils, interleukin (IL)-4, IL-5 and IL-13 playing important roles. While clearly asthma is a complex disease, some of the noise in the literature surrounding the determinants of asthma is a result of disease definition. The presence of wheezing or reduced FEV1 may represent a number of endotypes of asthma or indeed non-asthma phenotypes. With this caveat in mind, it is clear that viral infections and allergic sensitisation are key factors associated with a diagnosis of asthma.3 Numerous epidemiological and experimental studies have provided this link. A popular paradigm postulates that recurrent respiratory viral infections at critical time periods of immune and lung development in childhood and infancy coupled with allergic sensitisation are associated with the development of asthma.4 What is often questioned is the direction of this association; is a child with an underlying asthma phenotype more likely to experience viral infections and develop allergy? Or is an otherwise healthy child who happens to get viral infections subsequently pushed into an asthma phenotype? Like many ‘either – or’ questions, the answer is likely ‘yes’; that is, either possibility or neither may be operational in different situations. Indeed, given that asthma is so heterogeneous, it is logical that there may be many ways to reach this diagnosis. In Thorax …
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".