Patient-managed interventions for adults with bronchiectasis: evidence, challenges and prospects
Bibliographic record
Abstract
Bronchiectasis is a chronic lung condition which is characterised by recurrent chest infections, chronic sputum production and cough, and limited exercise tolerance. While bronchiectasis may be caused by various aetiologies, these features are shared by most patients with bronchiectasis regardless of the cause. This review consolidates the existing evidence on patient-managed interventions for adults with bronchiectasis, while also outlining areas for future research. Airway clearance techniques and hyperosmolar agents are key components of the bronchiectasis management and consistently recommended for clinical implementation. Questions around their prescription, such as optimal sequence of delivery, are still to be answered. Pulmonary rehabilitation and exercise are also recommended for patients with bronchiectasis. Relatively strong evidence underpins this recommendation during a clinically stable stage of the disease, although the role of pulmonary rehabilitation following an exacerbation is still unclear. Additionally, self-management programmes feature prominently in bronchiectasis treatment, yet the lack of consensus regarding their definition and outcomes presents hurdles to establishing a cohesive evidence base. Moreover, cough, a cardinal symptom of bronchiectasis, warrants closer examination. Although managing cough in bronchiectasis may initially appear risky, further research is necessary to ascertain whether strategies employed in other respiratory conditions can be safely and effectively adapted to bronchiectasis, particularly through identifying patient responder populations and criteria where cough may not enhance airway clearance efficacy and its control is needed. Overall, there is a growing recognition of the importance of patient-managed interventions in the bronchiectasis management. Efforts to improve research methodologies and increase research funding are needed to further advance our understanding of these interventions, and their role in optimising patient care and 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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".