International perspective on research priorities and outcome measures of importance in the care of children with acute exacerbations of asthma: a qualitative interview study
Bibliographic record
Abstract
BACKGROUND: Acute exacerbations of asthma are common in children, however, treatment decisions for severe exacerbations are challenging due to a lack of robust evidence. In order to create more robust research, a core set of outcome measures needs to be developed. In developing these outcomes, it is important to understand the views of clinicians who care for these children in particular, views that relate to outcome measures and research priorities. METHODS: To determine the views of clinicians, a total of 26 semistructured interviews based on the theoretical domains framework were conducted. These included experienced clinicians from emergency, intensive care and inpatient paediatrics across 17 countries. The interviews were recorded, and later transcribed. All data analyses were conducted in Nvivo by using thematic analysis. RESULTS: The length of stay in hospital and patient-focused parameters, such as timing to return to school and normal activity, were the most frequently highlighted outcome measures, with clinicians identifying the need to achieve a consensus on key core outcome measure sets. Most research questions focused on understanding the best treatment options, including the role of novel therapies and respiratory support. CONCLUSION: Our study provides an insight into what research questions and outcome measures clinicians view as important. In addition, information on how clinicians define asthma severity and measure treatment success will assist with methodological design in future trials. The current findings will be used in parallel with a further Paediatric Emergency Research Network study focusing on the child and family perspectives and will contribute to develop a core outcome set for future research.
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 | 0.070 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".