Personalising airway clearance in chronic lung disease
Bibliographic record
Abstract
This review describes a framework for providing a personalised approach to selecting the most appropriate airway clearance technique (ACT) for each patient. It is based on a synthesis of the physiological evidence that supports the modulation of ventilation and expiratory airflow as a means of assisting airway clearance. Possession of a strong understanding of the physiological basis for ACTs will enable clinicians to decide which ACT best aligns with the individual patient's pathology in diseases with anatomical bronchiectasis and mucus hypersecretion.The physiological underpinning of postural drainage is that by placing a patient in various positions, gravity enhances mobilisation of secretions. Newer ACTs are based on two other physiological premises: the ability to ventilate behind obstructed regions of the lung and the capacity to achieve the minimum expiratory airflow bias necessary to mobilise secretions. After reviewing each ACT to determine if it utilises both ventilation and expiratory flow, these physiological concepts are assessed against the clinical evidence to provide a mechanism for the effectiveness of each ACT. This article provides the clinical rationale necessary to determine the most appropriate ACT for each patient, thereby improving care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".