Perspective: Using Bronchiectasis Action Management Plans for Children With Bronchiectasis—Can It Improve Clinical Care?
Bibliographic record
Abstract
While once thought to be rare, bronchiectasis has been increasing globally over the last 15 years. Bronchiectasis is a major contributor to chronic lung morbidity and mortality but remains a neglected disease in respiratory health globally. Currently, few high-level evidence-based management strategies are available for children with bronchiectasis. Strategies to improve clinical outcomes associated with exacerbations are important. In other respiratory conditions such as asthma and chronic obstructive pulmonary disease, use of personalized written management plans have been shown to improve clinical outcomes. Personalized management plans have also been recommended as part of treatment plans in adults with bronchiectasis. We thus undertook a review of the current literature to determine available evidence, and to establish whether a personalized written bronchiectasis action management plan (BAMP) improves clinical outcomes in children with bronchiectasis. Our search identified 43 articles; 16 duplicates were removed and a further 23 were excluded on titles and abstracts alone. Four full-text articles were reviewed but excluded. In the absence of any published studies, it remains unknown whether the use of BAMP is beneficial for improving clinical outcomes for children with bronchiectasis. These results have highlighted this clinical gap and identified the need for high-quality research to inform practice. Until high-quality evidence is available, clinicians are advised to adhere to current national and/or international guidelines.
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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 | 0.014 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".