Pediatric asthma: An unmet need for more effective, focused treatments
Bibliographic record
Abstract
BACKGROUND: Despite remarkable advances in our understanding of asthma, there are still several unmet needs associated with the management of pediatric asthma. METHODS: A two-day, face-to-face meeting was held in London, United Kingdom, on October 28 and 29, 2017, involving a group of international expert clinicians and scientists in asthma management to discuss the challenges and unmet needs that remain to be addressed in pediatric asthma. RESULTS: These unmet needs include a lack of clinical efficacy and safety evidence, and limited availability of non-steroid-based alternative therapies in patients <6 years of age. An increased focus on children is needed in the context of clinical practice guidelines for asthma; current pediatric practice relies mostly on extrapolations from adult recommendations. Furthermore, no uniform definition of pediatric asthma exists, which hampers timely and robust diagnosis of the condition in affected patients. CONCLUSIONS: There is a need for a uniform definition of pediatric asthma, clearly distinguishable from adult asthma. Furthermore, guidelines which provide specific treatment recommendations for the management of pediatric asthma are also needed. Clinical trials and real-world evidence studies assessing anti-immunoglobulin E (IgE) therapies and other monoclonal antibodies in children <6 years of age with asthma may provide further information regarding the most appropriate treatment options in these vulnerable patients. Early intervention with anti-IgE and non-steroid-based alternative therapies may delay disease progression, leading to improved clinical outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".