Ventilator hyperinflation in paediatric critical care: a survey of current physiotherapy practice in the United Kingdom and Ireland
Bibliographic record
Abstract
Introduction: \nPhysiotherapists in paediatric intensive care units (PICUs) use a variety of techniques to remove retained bronchopulmonary secretions and improve work of breathing in children who are mechanically ventilated. Ventilator hyperinflation (VHI) is commonly used in adults to aid secretion removal without disrupting the integrity of the ventilatory circuit. This study aimed to identify current practice of VHI within paediatrics in the United Kingdom (UK) and Ireland. \n \nMethods: \nA survey was designed and distributed via email to senior physiotherapists in all 22 PICUs across the UK and Ireland. Physiotherapists working in adult critical care were excluded. Responses were analysed via descriptive statistics, with content analysis used for free text open questions. \n \nResults: \nTwenty-nine surveys were completed, of which 17 individuals (58%) indicated that they used VHI. VHI was used infrequently (commonly less than once per month, N=13 76.5%) and techniques were generally taught at the bedspace by senior colleagues. Indications for using VHI rather than manual hyperinflations included concerns over de-recruitment on disconnection from the ventilator (N=11, 64.8%), patients with COVID-19 and those with a high respiratory infection risk (N=8, 47%). Approaches to applying VHI varied, with target peak inspiratory pressures between 28cmH2O and 42cmH2O. \n \nConclusion: \nThe survey responses returned suggest that the use of VHI in PICU is infrequent, with no standard approach to its use. However, response rate is unknown owing to survey distribution method. There appear to be some occasions where respondents would choose VHI over manual hyperinflations and further research is needed to explore these further.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".