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Record W2602864176 · doi:10.1111/apa.13839

Neurophysiological assessment of acute pain in infants: a scoping review of research methods

2017· review· en· W2602864176 on OpenAlexafffund
Britney Benoit, Ruth Martin‐Misener, Aaron J. Newman, Margot Latimer, Marsha Campbell‐Yeo

Bibliographic record

VenueActa Paediatrica · 2017
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
FundersCanadian Institutes of Health ResearchNova Scotia Health Research FoundationCanadian Child Health Clinician Scientist Program
KeywordsMedicineAcute painIntensive care medicinePain assessmentPhysical therapyPain managementAnesthesia

Abstract

fetched live from OpenAlex

A systematic scoping search to describe the neurophysiological methods used in infant acute pain assessment research was conducted. Of the 2411 abstracts screened, 19 articles were retained. Nine studies utilised near-infrared spectroscopy (NIRS), two utilised functional magnetic resonance imaging (fMRI), and eight utilised electroencephalography (EEG). There was methodological variability in studies utilising NIRS, whereas EEG and fMRI studies reported consistent methods. Of the eight EEG studies, six identified a nociceptive-specific event-related potential. CONCLUSION: While more methodologically rigorous studies are needed, ERPs appear to hold some promise as indicators of infant nociception during clinical procedures to supplement existing measures.

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)0.013
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.052
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0200.017
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.261
GPT teacher head0.601
Teacher spread0.340 · 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 designSystematic review
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

Citations57
Published2017
Admission routes2
Has abstractyes

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