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Record W2889676659 · doi:10.1055/s-0038-1669442

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

2018· article· en· W2889676659 on OpenAlexaboutno aff
Ashish Gupta, Martin Keszler

Bibliographic record

VenueAmerican Journal of Perinatology · 2018
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVentilation (architecture)Cross-sectional studyIntensive careEmergency medicineIntensive care medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.073
GPT teacher head0.367
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2018
Admission routes1
Has abstractyes

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