Problematic severe asthma in children, not one problem but many: a GA<sup>2</sup>LEN initiative
Bibliographic record
Abstract
Although most children with asthma are easy to treat with low doses of safe medications, many remain symptomatic despite every therapeutic effort. The nomenclature regarding this group is confusing, and studies are difficult to compare due to the proliferation of terms describing poorly defined clinical entities. In this review of severe asthma in children, the term problematic severe asthma is used to describe children with any combination of chronic symptoms, acute severe exacerbations and persistent airflow limitation despite the prescription of multiple therapies. The approach to problematic severe asthma may vary with the age of the child, but, in general, three steps need to be taken in order to separate difficult-to-treat from severe therapy-resistant asthma. First, confirmation that the problem is really due to asthma requires a complete diagnostic re-evaluation. Secondly, the paediatrician needs to systematically exclude comorbidity, as well as personal or family psychosocial disorders. The third step is to re-evaluate medication adherence, inhaler technique and the child's environment. There is a clear need for a common international approach, since there is currently no uniform agreement regarding how best to approach children with problematic severe asthma. An essential first step is proper attention to basic care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".