Quantity and quality of airway clearance in children and young people with cystic fibrosis
Bibliographic record
Abstract
Children and young people with CF (CYPwCF) get advice about using positive expiratory pressure (PEP) or oscillating PEP (OPEP) devices to clear sticky mucus from their lungs. However, little is known about the quantity (number of treatments, breaths, or sets) or quality (breath pressures and lengths) of these daily airway clearance techniques (ACTs) undertaken at home. This study used electronic pressure sensors to record real time breath-by-breath data from 145 CYPwCF (6-16y) during routine ACTs over 2 months. ACT quantity and quality were benchmarked against individual prescriptions and accepted recommendations for device use. In total 742,084 breaths from 9,081 treatments were recorded. Individual CYPwCF maintained consistent patterns of ACT quantity and quality over time. Overall, 60% of CYPwCF did at least half their prescribed treatments, while 27% did fewer than a quarter. About 77% of pre-teens did the right number of daily treatments compared with only 56% of teenagers. CYPwCF usually did the right number of breaths. ACT quality (recommended breath length and pressure) varied between participants and depended on device. Breath pressures, lengths and pressure-length relationships were significantly different between ACT devices. PEP devices encouraged longer breaths with lower pressures, while OPEP devices encouraged shorter breaths with higher pressures. More breaths per treatment were within advised ranges for both pressure and length using PEP (30-31%) than OPEP devices (1-3%). Objective measures of quantity and quality may help to optimise ACT device selection and support CYPwCF to do regular effective ACTs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".