Lung volume changes during apnoeas in preterm infants
Bibliographic record
Abstract
Objective Mechanisms of non-invasive high-frequency oscillatory ventilation (nHFOV) in preterm infants are unclear. We aimed to compare lung volume changes during apnoeas in preterm infants on nHFOV and nasal continuous positive airway pressure (nCPAP). Methods Analysis of electrical impedance tomography (EIT) data from a randomised crossover trial comparing nHFOV with nCPAP in preterm infants at 26–34 weeks postmenstrual age. EIT data were screened by two reviewers to identify apnoeas ≥10 s. End-expiratory lung impedance (EELI) and tidal volumes (V T ) were calculated before and after apnoeas. Oxygen saturation (SpO 2 ) and heart rate (HR) were extracted for 60 s after apnoeas. Results In 30 preterm infants, 213 apnoeas were identified. During apnoeas, oscillatory volumes were detectable during nHFOV. EELI decreased significantly during apnoeas (∆EELI nCPAP: −8.0 (−11.9 to −4.1) AU/kg, p<0.001; ∆EELI nHFOV: −3.4 (−6.5 to −0.3), p=0.03) but recovered over the first five breaths after apnoeas. Compared with before apnoeas, V T was increased for the first breath after apnoeas during nCPAP (∆V T : 7.5 (3.1 to 11.2) AU/kg, p=0.001). Falls in SpO 2 and HR after apnoeas were greater during nCPAP than nHFOV (mean difference (95% CI): SpO 2 : 3.6% (2.7 to 4.6), p<0.001; HR: 15.9 bpm (13.4 to 18.5), p<0.001). Conclusion Apnoeas were characterised by a significant decrease in EELI which was regained over the first breaths after apnoeas, partly mediated by a larger V T . Apnoeas were followed by a considerable drop in SpO 2 and HR, particularly during nCPAP, leading to longer episodes of hypoxemia during nCPAP. Transmitted oscillations during nHFOV may explain these benefits. Trial registration number ACTRN12616001516471.
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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 | 0.011 |
| 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) | 0.000 | 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".