Respiratory medicines for children: current evidence, unlicensed use and research priorities
Bibliographic record
Abstract
This European Respiratory Society task force has reviewed the evidence for paediatric medicines in respiratory disease occurring in adults and children. We describe off-licence use, research priorities and ongoing studies. Off-licence and off-label prescribing in children is widespread and potentially harmful. Research areas in asthma include novel formulations and regimens, and individualised prescribing. In cystic fibrosis, future studies will focus on screened infants and robust outcome measures are needed. Other areas include new enzyme and antibiotic formulations and the basic defect. Research into pneumonia should include evaluation of new antibacterials and regimens, rapid diagnostic tests and, in pleural infection, antibiotic penetration, fibrinolytics and surveillance. In uncommon conditions, such as primary ciliary dyskinesia, congenital pulmonary abnormalities or neuromuscular disorders, drugs indicated for other conditions (e.g. dornase alfa) are commonly used and trials are needed. In neuromuscular disorders, the beta-agonists may enhance muscle strength and are in need of evaluation. Studies of antibiotic prophylaxis, immunoglobulin and antifungal drugs are needed in immune deficiency. We hope that this summary of the evidence for respiratory medicines in children, highlighting gaps and research priorities, will be useful for the pharmaceutical industry, the paediatric committee of the European Medicines Agency, academic investigators and the lay public.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".