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Record W2166510429 · doi:10.1111/apa.12998

High‐flow nasal cannulae are associated with increased diaphragm activation compared with nasal continuous positive airway pressure in preterm infants

2015· article· en· W2166510429 on OpenAlexaff
Nehad Nasef, Enas El‐Gouhary, Patti Schurr, Maureen Reilly, Jennifer Beck, Michael Dunn

Bibliographic record

VenueActa Paediatrica · 2015
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversity of TorontoSt. Michael's HospitalHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineContinuous positive airway pressureAnesthesiaDiaphragm (acoustics)AirwayObstructive sleep apnea

Abstract

fetched live from OpenAlex

AIM: High-flow nasal cannulae (HFNC) are increasingly used for respiratory management of preterm infants. However, their ability to provide support compared to nasal continuous positive airway pressure (CPAP) has been questioned. We compared the effect of HFNC versus nasal CPAP on diaphragm electrical activity (EAdi) in preterm infants. METHODS: Preterm infants ≤1500 g were randomised in a crossover design to receive 2 hours of either Infant Flow(®) CPAP (IF-CPAP) at 5-6 cmH2 O or HFNC with the flow rate adjusted to achieve an equivalent pharyngeal pressure. A feeding catheter with miniaturised sensors was inserted for continuous EAdi measurement. RESULTS: The study comprised ten infants. Physiologic parameters and oxygen requirements were not different between the two modes. However, seven infants demonstrated a higher EAdi peak and six showed a higher EAdi tonic on HFNC, even though the mean group data showed no difference between HFNC and IF-CPAP. Neural inspiratory time was significantly longer with HFNC than IF-CPAP (0.55 ± 0.11 versus 0.48 ± 0.06 seconds, p = 0.018). CONCLUSION: In this cohort of preterm infants, the majority exhibited greater diaphragm activation, as assessed by neural breathing patterns, when supported with HFNC than IF-CPAP, suggesting that nasal CPAP may provide more effective respiratory support.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.020
GPT teacher head0.282
Teacher spread0.262 · 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

Citations26
Published2015
Admission routes1
Has abstractyes

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