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Record W4311114241 · doi:10.1038/s41372-022-01576-2

Abdominal Near Infrared Spectroscopy can be reliably used to measure splanchnic oxygenation changes in preterm infants

2022· article· en· W4311114241 on OpenAlexfundno aff
R. A. Thomas, M-R Ballard, Narendra Aladangady, Jayanta Banerjee

Bibliographic record

VenueJournal of Perinatology · 2022
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsnot available
FundersNational Institute for Health and Care ResearchHamamatsu Photonics K.K.W. Garfield Weston Foundation
KeywordsMedicineSplanchnicOxygenationGestational agePerfusionAnesthesiaOxygenation indexCardiologyHemodynamicsPregnancy

Abstract

fetched live from OpenAlex

OBJECTIVE: Near-infrared spectroscopy (NIRS) allows assessment of regional tissue oxygen delivery and extraction. There are doubts regarding reliability of gut NIRS measurements. This study assesses reliability of NIRS for monitoring gut oxygenation. STUDY DESIGN: Splanchnic tissue haemoglobin index (sTHI), tissue oxygenation index (sTOI) and fractional tissue oxygen extraction (sFTOE) changes during blood transfusion were measured using NIRS and compared to stable control infants. Infants were grouped into 3 chronological age groups: 1-7, 8-28 and ≥29 days of life. RESULTS: sTHI, sTOI significantly increased, and sFTOE reduced following blood transfusion in all age group infants (n = 59), with no changes seen in control infants (n = 12). Baseline characteristics including gestational age and feed volumes did not differ between groups. CONCLUSION: Gut perfusion measured by NIRS improved in infants who received blood transfusion, a change not seen in the control group, thus suggesting NIRS is a reliable method to measure splanchnic tissue oxygenation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.022
GPT teacher head0.317
Teacher spread0.295 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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