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Record W2762463831 · doi:10.1093/pch/20.5.e67

90: Flow Sensor Versus End-Tidal Carbon Dioxide to Identified Correct Endotracheal Tube Placement in Newborn Infants – A Randomized Controlled Trial

2015· article· en· W2762463831 on OpenAlexaff
Georg M. Schmölzer, Sylvia van Os, Po‐Yin Cheung, Michael O’Reilly, K Kushniruk, Khalid Aziz

Bibliographic record

VenuePaediatrics & Child Health · 2015
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsAlberta Hospital Edmonton
Fundersnot available
KeywordsMedicineIntubationNeonatal intensive care unitAnesthesiaRandomized controlled trialEndotracheal intubationPneumothoraxGestational agePediatricsSurgeryPregnancy

Abstract

fetched live from OpenAlex

Endotracheal intubation remains a common procedure in the neonatal intensive care unit (NICU). Rapid confirmation of endotracheal tube (ETT) placement at the point of care is important because tube malposition is associated with serious adverse outcomes, including hypoxemia, pneumothorax, right upper lobar collapse and death. An observational study recently reported that the PediCap® incorrectly identified tube position in up to one third of the analyzed intubations. To determine if the use of a flow sensor (VN500, Draeger Luebeck, Germany) compared to a CO2 detector (Philips, Markham, ON) will improve confirmation of ETT placement in newborn infants within the first 10 inflations. All term and preterm infants admitted to Royal Alexandra Hospital NICU who require endotracheal intubation were eligible. Infants were randomized to have ETT assessed by flow sensor or by CO2 detector. The primary outcome was number of inflations delivered before confirmation of ETT. The trial was registered at ClinicalTrials.gov: NCT01870622. 100 infants were randomized (n=50 for each group); the mean (range) gestational age was 28 (23–41) weeks and birth weight was 1213 (490–4000) g. Number of intubation attempts ranged from 1 to 4 for all infants. In 25% the intubation was performed for surfactant administration with extubation shortly afterwards, while in 75% the infants were intubated for mechanical ventilation. Mean (range) number of inflations needed to identify correct ETT was significantly lower in the flow sensor group with 2 (1–10) inflations vs. 8 (2–30) inflations in the CO2 detector group (P<0.001). ETT were correctly identified in 100% by the flow sensor within 10 inflations compared to 72% with the CO2-detector (P<0.05). The lowest heart rate and oxygen saturation at end of intubation in the CO2 detector and flow sensor group were 148 (28)/min vs. 159 (18) (P=0.03) and 71 (23) vs. 80 (17) (P=0.043), respectively. Using a flow sensor significantly decreases the time to correctly identify ETT in newborn infants compared to using CO2 detector.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.042
GPT teacher head0.358
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations2
Published2015
Admission routes1
Has abstractyes

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