The feasibility of using electrical impedance tomography to guide positive pressure airway clearance in children with cystic fibrosis and tracheobronchomalacia
Bibliographic record
Abstract
Aim: Positive expiratory pressure devices are frequently used for airway clearance in children with cystic fibrosis and tracheobronchomalacia. This study aimed to establish if electrical impedance tomography is a feasible measure to titrate pressures in non-sedated children. Method: Ten children with cystic fibrosis and tracheobronchomalacia performed airway clearance using positive pressure devices whilst monitored with electrical impedance tomography. Feasibility was measured as tolerability and completion of the intervention; ease of administration and interpretation of software data; ability to determine differences in regional lung ventilation; and compatibility for use in the clinic. A pre-determined score of ≥70% was deemed successful for each measure and reported as means and ranges. Results: Criterion met success for tolerability (98%; 86-100) and intervention completion (95%; 90-100). Regions of interest display (96%; 80-100) and software data analysis (96%; 90-100) allowed regional lung ventilation changes to be observed with different pressures. Ease of administration and compatibility for the clinic highlighted difficulties with automated software functionality and clinician time (66%; 10-100% and 75%; 0-100). Conclusion: Use of electrical impedance tomography is feasible in non-sedated children with cystic fibrosis. It has potential as a tool for guiding positive pressure titration for airway clearance. Results and application to clinical practice require further study.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".