Imaging the Respiratory Transition at Birth: Unraveling the Complexities of the First Breaths of Life
Bibliographic record
Abstract
Abstract Rationale The transition to air breathing at birth is a seminal respiratory event common to all humans, but the intrathoracic processes remain poorly understood. Objectives The objectives of this prospective, observational study were to describe the spatiotemporal gas flow, aeration, and ventilation patterns within the lung in term neonates undergoing successful respiratory transition. Methods Electrical impedance tomography was used to image intrathoracic volume patterns for every breath until 6 minutes from birth in neonates born by elective cesearean section and not needing resuscitation. Breaths were classified by video data, and measures of lung aeration, tidal flow conditions, and intrathoracic volume distribution calculated for each inflation. Measurements and Main Results A total of 1,401 breaths from 17 neonates met all eligibility and data analysis criteria. Stable FRC was obtained by median (interquartile range) 43 (21–77) breaths. Breathing patterns changed from predominantly crying (80.9% first min) to tidal breathing (65.3% sixth min). From birth, tidal ventilation was not uniform within the lung, favoring the right and nondependent regions; P < 0.001 versus left and dependent regions (mixed-effects model). Initial crying created a unique volumetric pattern with delayed midexpiratory gas flow associated with intrathoracic volume redistribution (pendelluft flow) within the lung. This preserved FRC, especially within the dorsal and right regions. Conclusions The commencement of air breathing at birth generates unique flow and volume states associated with marked spatiotemporal ventilation inhomogeneity not seen elsewhere in respiratory physiology. At birth, neonates innately brake expiratory flow to defend FRC gains and redistribute gas to less aerated regions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".