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Record W2972264259 · doi:10.1055/s-0039-1695776

Prone versus Supine Position for Lung Ultrasound in Neonates with Respiratory Distress

2019· article· en· W2972264259 on OpenAlexaff
Deepak Louis, Karen Belen, Mansoor Farooqui, Nnanake Idiong, Reem Amer, A. Hussain, Yasser Elsayed

Bibliographic record

VenueAmerican Journal of Perinatology · 2019
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineSupine positionRespiratory distressLung ultrasoundProne positionRespiratory systemLungUltrasoundPosition (finance)AnesthesiaInternal medicineRadiology

Abstract

fetched live from OpenAlex

Objective To study the feasibility of lung ultrasound (LUS) in prone position and to compare it with supine position in neonates with respiratory distress. Study Design Neonates ≥ 29 weeks of gestational age with respiratory distress requiring respiratory support within first 12 hours of life were enrolled prospectively. First LUS (fLUS) was done in the position infant was nursed (supine or prone), infant’s position changed, a second LUS (sLUS) was performed immediately and a third LUS (tLUS) was done 1 to 2 hours later. Primary outcome was the comparison of LUS scores (LUSsc) between fLUS and sLUS. Results Sixty-four neonates were enrolled. Common respiratory diagnoses were transient tachypnea of newborn (TTN; 53%) and respiratory distress syndrome (RDS; 41%). LUSsc was different between fLUS and sLUS (fLUSsc 6 [interquatile range: 4, 7] vs. sLUSsc 7 [4, 10], p < 0.001), while there was no difference between the fLUS and tLUS (fLUSsc 6 [4, 7] vs. tLUSsc 5 [3, 7], p = 0.43). Subgroup analysis confirmed similar findings in neonates with TTN, while in babies with RDS, all the three LUSsc were similar. Conclusion LUS is feasible in prone position in neonates. LUS scores were higher immediately after a change in position but were similar to baseline 1 hour after the change in position.

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)0.000
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 score0.174
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.336
Teacher spread0.321 · 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

Citations43
Published2019
Admission routes1
Has abstractyes

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