Intermittent sigh breaths during high-frequency oscillatory ventilation in preterm infants: a randomised crossover study
Bibliographic record
Abstract
OBJECTIVE: To determine if combining high-frequency oscillatory ventilation (HFOV) with additional sigh breaths would improve end-expiratory lung volume (EELV) and oxygenation in preterm infants. DESIGN: Prospective interventional crossover study. SETTING: Neonatal intensive care unit. PATIENTS: Ventilated preterm infants <36 weeks corrected gestational age receiving HFOV. INTERVENTIONS: 0 and frequency of three breaths/min. MAIN OUTCOME MEASURES: Electrical impedance tomography measured the effect of sigh breaths on EELV and ventilation distribution. Physiological variables were recorded to monitor oxygenation. Measurements were taken at 30 and 60 min postchange of HFOV mode and compared with baseline. RESULTS: Sixteen infants (10 males, 6 females) with a median (range) gestational age at birth of 25.5 weeks (23-31), study weight of 950 g (660-1920) and a postnatal age of 25 days (3-49) were included in the study. The addition of sigh breaths resulted in a significantly higher global EELV (mean difference±95% CI) (0.06±0.05; p=0.04), with increased ventilation occurring in the posterior (dependent) and left lung segments, and improved oxygen saturations (3.31±2.10; p<0.01). CONCLUSION: Intermittent sigh breaths during HFOV were associated in the short-term with an increased EELV in the posterior and left lungs, and improved oxygen saturations in preterm infants.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".