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Record W2008937965 · doi:10.1089/neu.2007.0474

Fluctuations in Cortical Synchronization in Pediatric Traumatic Brain Injury

2008· article· en· W2008937965 on OpenAlexafffund
Vera Nenadovic, James S. Hutchison, Luís Garcia Dominguez, Hiroshi Otsubo, Martin Gray, Rohit Sharma, Jason S. Belkas, José Luis Pérez Velázquez

Bibliographic record

VenueJournal of Neurotrauma · 2008
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersSick Kids FoundationEli Lilly and Company
KeywordsGlasgow Coma ScaleTraumatic brain injuryMedicineComa (optics)ElectroencephalographyHead injuryPopulationPediatricsAnesthesiaSurgeryPsychiatry

Abstract

fetched live from OpenAlex

Traumatic brain injury (TBI) is the leading cause of death and acquired disability in the pediatric population worldwide. We hypothesized that electroencephalography (EEG) synchrony and its temporal variability, analyzed during the acute phase following TBI, would be altered from that of normal children and as such would offer insights into TBI pathophysiology. Seventeen pediatric patients with mild to severe head injury admitted to a pediatric critical care unit were recruited along with 10 age- and gender-matched controls. Patients had two electroencephalographs performed 3 days apart. Outcome was measured at 1 year post-TBI utilizing the Pediatric Cerebral Performance Category score (PCPC). Maximal synchrony between EEG channels correlated to areas of primary injury as seen on computed tomography (CT) scan. The temporal variability of phase synchronization among EEG electrodes increased as patients recovered and emerged from coma (p < 0.001). This temporal variability correlated with outcome (Pearson coefficient of 0.74) better than the worst Glasgow Coma Scale score, length of coma, or extent of injury on CT scan. This represents a novel approach in the evaluation of TBI in children.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.051
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.138
GPT teacher head0.388
Teacher spread0.250 · 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 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

Citations42
Published2008
Admission routes2
Has abstractyes

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