Improving shared decision-making in bronchiectasis
Bibliographic record
Abstract
Bronchiectasis is a heterogeneous lung disease. There is an increasing focus on personalised medicine in bronchiectasis, with targeted pharmacological interventions for inflammation, immunology and infection. Airway clearance techniques (ACTs) are non-pharmacological treatments used to manage bronchiectasis. Approximately half of patients with bronchiectasis perform ACTs. There have been attempts to personalise ACT prescriptions, including consideration of patient physiology, disease status and psychosocial factors. Guidelines suggest that patient preference or choice should be considered when prescribing ACTs. There is a lack of literature showing patient preference or choice being taken into consideration when prescribing ACTs in bronchiectasis. This article discusses the role of shared decision-making (SDM), the potential use of SDM for ACTs in bronchiectasis to support patient choice of and adherence to ACTs and the steps involved in designing an SDM intervention for ACTs in bronchiectasis for future research. Development and use of an SDM intervention to support patient choice of ACT in bronchiectasis may result in a patient-centred, pragmatic approach to empower patients to be actively involved in their care, improve their knowledge on the importance of ACTs and support improvement in adherence to this essential therapy.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".