Lung volume distribution in preterm infants on non-invasive high-frequency ventilation
Bibliographic record
Abstract
Introduction Non-invasive high-frequency oscillatory ventilation (nHFOV) is an extension of nasal continuous positive airway pressure (nCPAP) support in neonates. We aimed to compare global and regional distribution of lung volumes during nHFOV versus nCPAP. Methods In 30 preterm infants enrolled in a randomised crossover trial comparing nHFOV with nCPAP, electrical impedance tomography data were recorded in prone position. For each mode of respiratory support, four episodes of artefact-free tidal ventilation, each comprising 30 consecutive breaths, were extracted. Tidal volumes (VT) in 36 horizontal slices, indicators of ventilation homogeneity and end-expiratory lung impedance (EELI) for the whole lung and for four horizontal regions of interest (non-gravity-dependent to gravity-dependent; EELINGD, EELImidNGD, EELImidGD, EELIGD) were compared between nHFOV and nCPAP. Aeration homogeneity ratio (AHR) was determined by dividing aeration in non-gravity-dependent parts of the lung through gravity-dependent regions. Main results Overall, 228 recordings were analysed. Relative VT was greater in all but the six most gravity-dependent lung slices during nCPAP (all p<0.05). Indicators of ventilation homogeneity were similar between nHFOV and nCPAP (all p>0.05). Aeration was increased during nHFOV (mean difference (95% CI)=0.4 (0.2 to 0.6) arbitrary units per kilogram (AU/kg), p=0.013), mainly due to an increase in non-gravity-dependent regions of the lung (∆EELINGD=6.9 (0.0 to 13.8) AU/kg, p=0.028; ∆EELImidNGD=6.8 (1.2 to 12.4) AU/kg, p=0.009). Aeration was more homogeneous during nHFOV compared with nCPAP (mean difference (95% CI) in AHR=0.01 (0.00 to 0.02), p=0.0014). Conclusion Although regional ventilation was similar between nHFOV and nCPAP, end-expiratory lung volume was higher and aeration homogeneity was slightly improved during nHFOV. The aeration difference was greatest in non-gravity dependent regions, possibly due to the oscillatory pressure waveform. The clinical importance of these findings is still unclear.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".