Survey of Ventilation Practices in the Neonatal Intensive Care Units of the United States and Canada: Use of Volume-Targeted Ventilation and Barriers to Its Use
Bibliographic record
Abstract
Objective To provide current data on ventilation practices and use of volume-targeted ventilation (VTV) in neonatal intensive care units of the United States and Canada, to identify the perceived barriers to the implementation of VTV, and to assess the knowledge base of appropriate initial tidal volume (VT ) settings for different hypothetical clinical scenarios. Study Design This was a cross-sectional online survey of individual neonatologists practicing in the United States and Canada. Results We received 387 responses (estimated response rate: ∼20%). Use of VTV was much higher in Canada (81%) compared with 39% in the United States. In the United States, VTV use is highest in the Northwest at 77% and lowest in the Northeast at 32.5%. The chief barrier to use of VTV was lack of knowledge about VTV and lack of appropriate equipment. The five clinical scenarios revealed that the majority of responders failed to select appropriate evidence-based VT for the specific scenario. Conclusion Pressure-controlled ventilation remains the predominant approach to neonatal ventilation in the United States, while VTV is the preferred mode in Canada. Despite available data and important pathophysiological differences between patients, there is insufficient understanding of how to choose an appropriate VT in a variety of common clinical scenarios among users of VTV.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".