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Record W4399693852 · doi:10.1016/j.semperi.2024.151928

Effects of pain, sedation and analgesia on neonatal brain injury and brain development

2024· review· en· W4399693852 on OpenAlexaff
Thiviya Selvanathan, Steven P. Miller

Bibliographic record

VenueSeminars in Perinatology · 2024
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of British ColumbiaHospital for Sick ChildrenBC Children's HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineSedationNeonatal intensive care unitSedativeAnalgesicIntensive care medicineAnesthesiaPediatrics

Abstract

fetched live from OpenAlex

Critically ill newborns experience numerous painful procedures as part of lifesaving care in the Neonatal Intensive Care Unit. However, painful exposures in the neonatal period have been associated with alterations in brain maturation and poorer neurodevelopmental outcomes in childhood. The most frequently used medications for pain and sedation in the NICU are opioids, benzodiazepines and sucrose; these have also been associated with abnormalities in brain maturation and neurodevelopment making it challenging to know what the best approach is to treat neonatal pain. This article provides clinicians with an overview of how neonatal exposure to pain as well as analgesic and sedative medications impact brain maturation and neurodevelopmental outcomes in critically ill infants. We also highlight areas in need of future research to develop standardized neonatal pain monitoring and management strategies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.012
GPT teacher head0.326
Teacher spread0.314 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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