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Record W2912223671 · doi:10.1177/0883073819829256

Combined Conventional and Amplitude-Integrated EEG Monitoring in Neonates: A Prospective Study

2019· article· en· W2912223671 on OpenAlexafffund
Sarah Grace Buttle, Brigitte Lemyre, Erick Sell, Stephanie Redpath, Srinivas Bulusu, Richard Webster, Daniela Pohl

Bibliographic record

VenueJournal of Child Neurology · 2019
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsElectroencephalographyAmplitudeNeurologyAudiologyEpilepsyMedicineEEG-fMRIPsychologyNeurosciencePhysics

Abstract

fetched live from OpenAlex

BACKGROUND/OBJECTIVE: Seizure monitoring via amplitude-integrated EEG is standard of care in many neonatal intensive care units; however, conventional EEG is the gold standard for seizure detection. We compared the diagnostic yield of amplitude-integrated EEG interpreted at the bedside, amplitude-integrated EEG interpreted by an expert, and conventional EEG. METHODS: Neonates requiring seizure monitoring received amplitude-integrated EEG and conventional EEG in parallel. Clinical events and amplitude-integrated EEG were interpreted at bedside. Subsequently, amplitude-integrated EEG and conventional EEG were independently analyzed by experienced neonatology and neurology readers. Sensitivity and specificity of bedside amplitude-integrated EEG as compared to expert amplitude-integrated EEG interpretation and conventional EEG were evaluated. RESULTS: Thirteen neonates were monitored for an average duration of 33 hours (range 15-94, SD 25). Fourteen seizure-like events were detected by clinical observation, and 12 others by bedside amplitude-integrated EEG analysis. One of the clinical, and none of the bedside amplitude-integrated EEG events were confirmed as seizures on conventional EEG. Post hoc expert amplitude-integrated EEG interpretation revealed eight suspected seizures, all different from the ones detected by the bedside amplitude-integrated EEG team, of which one was confirmed via conventional EEG. Eight seizures were recorded on conventional EEG. Expert amplitude-integrated EEG interpretation had a sensitivity of 13% with 46% specificity for individual seizure detection, and a sensitivity of 50% with 46% specificity for detecting patients with seizures. CONCLUSION: Real-world bedside amplitude-integrated EEG monitoring failed to detect all seizures evidenced via conventional EEG, while misclassifying other events as seizures. Even post hoc expert amplitude-integrated EEG interpretation provided limited sensitivity and specificity. Considering the poor sensitivity and specificity of bedside amplitude-integrated EEG interpretation, combined monitoring may provide limited clinical benefit.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.259
Teacher spread0.251 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

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