MétaCan
Menu
Back to cohort

Providing PEEP during neonatal resuscitation: Which device is best?

2011· article· en· W1795917594 on OpenAlexaff
Jennifer A. Dawson, Angela Gerber, C. Omar F. Kamlin, Peter G. Davis, Colin J. Morley

Bibliographic record

VenueJournal of Paediatrics and Child Health · 2011
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsRoyal Ottawa Mental Health Centre
FundersNational Health and Medical Research Council
KeywordsMedicineNeonatal resuscitationResuscitationLeakTidal volumePeak inspiratory pressureAnesthesiaPositive end-expiratory pressureVentilation (architecture)Positive pressure ventilationPositive pressureMechanical ventilationRespiratory systemInternal medicineMechanical engineering

Abstract

fetched live from OpenAlex

AIM: The study aims to compare three commonly used neonatal resuscitation devices, the Laerdal self-inflating bag with a positive end expiratory pressure (PEEP) valve, a T-piece resuscitator (T-piece) and a flow-inflating bag to provide peak inflation pressure (PIP) and PEEP. METHODS: Participants were asked to use each device to give positive pressure ventilation to a modified neonatal mannequin via a face mask to achieve 40-60 inflations per minute, aiming for a PIP/PEEP of 30/5 cm H₂O. A manometer was visible to participants with each device. PIP, PEEP, percentage leak at the face mask and expired tidal volume were measured using a hot-wire anemometer. We analysed 20 inflations from each participant for each device. RESULTS: Fifty participants provided PIP and PEEP with each device. The T-piece was the most accurate and consistent. The flow-inflating bag had the most variation. The leak was lowest with the self-inflating bag and PEEP and highest with the flow-inflating bag, but all had wide variation. CONCLUSION: Each device was able to provide PIP and PEEP when used appropriately. When compared with other resuscitation devices, the T-piece provided the most accurate and consistent PIP and PEEP.

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)0.002
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 score0.370
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.355
Teacher spread0.293 · 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

Citations74
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Paediatrics and Child HealthSame topicNeonatal Respiratory Health ResearchFrench-language works237,207