Predictors of response to medications for asthma in pediatric patients: A systematic review of the literature
Bibliographic record
Abstract
OBJECTIVES: There has been no systematic review of studies aimed to predict differential responses to medication regimens for asthma controller therapies in pediatric patients. The aim of the present study was to summarize those identifying biomarkers for the different asthma controller therapies. METHODS: Studies published by June 2019 that report phenotypic or genotypic characteristics or biomarkers that could potentially serve as response predictors to asthma controller therapies in pediatric patients were included. The quality of studies was assessed using the Cochrane Risk of Bias tool and the Newcastle-Ottawa Scale tool. RESULTS: Of 385 trials identified, 30 studies were included. Children with asthma and a positive family history of asthma, with more severe disease, of the white race, with allergy biomarkers, nonobese, with lower lung function, high bronchial hyperresponsiveness to methacholine, or having variants in the FCER2 and CRHR1 gene respond better to inhaled corticosteroids (ICS). Younger age (<10 years), short disease duration (<4 years), high cotinine and urinary leukotriene E4 (LTE4) levels, and 5/5 ALOX5 were associated with a better response to leukotriene receptor antagonist (LTRA). For patients that remain symptomatic, white Hispanics were more likely to respond to LTRA, blacks to ICS, white non-Hispanics to LTRA or LABA, and children without a history of eczema, regardless of race or ethnicity to LABA set-up therapy. In severe persistent asthma, those with atopy and body mass index greater than or equal 25 were more likely to benefit from omalizumab. CONCLUSION: Several phenotypic characteristics, biomarkers, or pharmacogenomics markers could be useful for predicting the best drug for asthma treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".