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Record W2890265242 · doi:10.3390/children5100132

Does the Number of Fingers on the Bag Influence Volume Delivery? A Randomized Model Study of Bag-Valve-Mask Ventilation in Infants

2018· article· en· W2890265242 on OpenAlexafffund
David Zweiker, Hanna Schwaberger, Berndt Urlesberger, Lukas P. Mileder, Nariae Baik‐Schneditz, Gerhard Pichler, Georg M. Schmölzer, Bernhard Schwaberger

Bibliographic record

VenueChildren · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsRoyal Alexandra HospitalUniversity of Alberta
FundersHeart and Stroke Foundation of Canada
KeywordsVentilation (architecture)MedicineRandomized controlled trialEngineeringSurgeryMechanical engineering

Abstract

fetched live from OpenAlex

We sought to compare the effectiveness of two versus five fingers used for bag-valve-mask (BVM) ventilation on effective tidal volume (VTeff) delivery in an infant resuscitation model. In a randomised cross-over study, 40 healthcare professionals ventilated a modified leak-free infant resuscitation manikin with both two and five fingers, using a self-inflating bag. The delivered and effective tidal volumes, ventilation rate, and mask leak were measured and recorded using a respiratory function monitor. We found no significant differences in the VTeff (five-finger 61.7 ± 23.9 vs. two-finger 58.8 ± 16.6 mL; p = 0.35) or ventilatory minute volume (2.71 ± 1.59 vs. 2.76 ± 1.24 L/min; p = 0.40) of both BVM ventilation techniques. However, there was an increase in the delivered tidal volume (VTdel) and mask leak when using the five-finger technique compared with the two-finger technique (VTdel 96.1 ± 19.4 vs. 87.7 ± 15.5 mL; p < 0.01; and mask leak 34.6 ± 23.0 vs. 30.0 ± 21.0%; p = 0.02). Although the five-finger technique was associated with an increased mask leak, the number of fingers used during the BVM ventilation had no effect on VTeff in an infant resuscitation model.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.270
Teacher spread0.263 · 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.

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

Citations4
Published2018
Admission routes2
Has abstractyes

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