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Record W4398762809 · doi:10.1038/s41372-024-01978-4

A comparison of the effect of procedural pain on cerebral oxygen saturation between late preterm and term infants

2024· article· en· W4398762809 on OpenAlexaff
Ittichote Amornjiraporn, Supranee Rugsapol, Peerawit Thanasarnpaiboon, Bosco Paes, Ratchada Kitsommart

Bibliographic record

VenueJournal of Perinatology · 2024
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcMaster University
FundersFaculty of Medicine Siriraj Hospital, Mahidol UniversityMahidol University
KeywordsMedicineGestational ageAnesthesiaPulse oximetryOxygen saturationPediatricsOxygenPregnancy

Abstract

fetched live from OpenAlex

OBJECTIVES: We prospectively compared cerebral oxygen saturation (CrSO2) and pain score changes during procedures in late preterm (LPT) versus term infants. METHODS: Near-infrared spectroscopy, pulse oximetry, Neonatal Infant Pain Scale (NIPS) and Premature Infant Pain Profile-Revised (PIPP-R) scores were assessed and CrSO2 data analyzed. RESULTS: Thirty infants in each group were evaluated. LPT infants displayed a milder significant drop in Minimum post-procedural CrSO2 and smaller Maximum-Minimum post-procedural CrSO2 disparity. CrSO2 minute changes between the groups were non-significant. Moderate correlations were observed in both groups between NIPS and Minimum post-procedural CrSO2, and a moderate correlation was found in the Maximum-Minimum post-procedural CrSO2 difference in LPT infants. No correlation between PIPP-R and CrSO2 values was noted. CONCLUSION: LPT and term infants demonstrated decreased CrSO2 in response to painful procedures. Correlations between CrSO2 and PIPP-R or NIPS scores were poor to moderate, reflecting the complex nature of these associations relative to gestational age.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0010.000

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 head0.013
GPT teacher head0.329
Teacher spread0.316 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

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