Interventions on Adherence to Treatment in Children With Severe Asthma: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: Poor adherence to inhaled medication is a commonly encountered problem among children with asthma. However, there is a relatively paucity of data regarding the adherence of children with severe asthma, as well as the merit of any interventions to improve this adherence. Objectives: The aim of this systematic review was to identify the available literature on the rate of adherence and the influence of interventions in improving adherence to controller inhaled medication, in children with severe asthma. Methods: A systematic literature search was performed in MEDLINE/PubMed, Cochrane Library, and Scopus databases. Studies were included in the present review if their target population were children and/or adolescents with severe asthma and presented data on medication adherence before and after a given intervention. Results: A total of seven studies, conducted in USA, Canada, and UK, and published between 2012 and 2018, met the inclusion criteria. Adherence to controller medication was assessed via either objective or subjective measures (questionnaires), or a combination of them. Interventions included communication during pediatric visits and audio-taped medical visits, individualized care programs, electronic monitoring devices, interactive website and peak–flow prediction with feedback. Adherence rates for the baseline (before intervention) or for the control groups ranged from 28% to 67%. In general, there was a significant improvement of adherence after intervention with rates increasing to 49%-81%. Conclusion: Adherence rate in children with severe asthma is not satisfactory but it can be improved after proper interventions. Nevertheless, the heterogeneity among adherence assessment tools, and the variety of interventions, in combination with the lack of studies focusing on severe asthma, highlight the need for further research in this field.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it