Respiratory Management of Extremely Preterm Infants: An International Survey
Bibliographic record
Abstract
BACKGROUND: There are significant international variations in chronic lung disease rates among very preterm infants yet there is little data on international variations in respiratory strategies. OBJECTIVE: To evaluate practice variations in the respiratory management of extremely preterm infants born at < 29 weeks' gestational age (GA) among 10 neonatal networks participating in the International Network for Evaluating Outcomes (iNeo) of Neonates collaboration. METHODS: A web-based survey was sent to the representatives of 390 neonatal intensive care units from Australia/New Zealand, Canada, Finland, Illinois (USA), Israel, Japan, Spain, Sweden, Switzerland, and Tuscany (Italy). Responses were based on practices in 2015. RESULTS: Overall, 321 of the 390 units responded (82%). The majority of units within networks (40-92%) mechanically ventilate infants born at 23-24 weeks' GA on continuous positive airway pressure (CPAP) with 30-39% oxygen in respiratory distress within 48 h after birth, but the proportion of units that offer mechanical ventilation for infants born at 25-26 weeks' GA at similar settings varied significantly (20-85% of units within networks). The most common respiratory strategy for infants born at 27-28 weeks' GA on CPAP with 30-39% oxygen with respiratory distress within 48 h after birth used by units also varied significantly among networks: mechanical ventilation (0-60%), CPAP (3-82%), intubation and surfactant administration with immediate extubation (0-75%), and less invasive surfactant administration (0-68%). CONCLUSIONS: There are marked variations but also similarities in respiratory management of extremely preterm infants between networks. Further collaboration and exploration is needed to better understand the association of these variations in practice with pulmonary outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".