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Record W2059624626 · doi:10.1089/neu.2006.0146

Impaired Percent Alpha Variability on Continuous Electroencephalography Is Associated with Thalamic Injury and Predicts Poor Long-Term Outcome after Human Traumatic Brain Injury

2007· article· en· W2059624626 on OpenAlexaff
Matthew O. Hebb, David L. McArthur, Jeffry R. Alger, Maria Etchepare, Thomas C. Glenn, Marvin Bergsneider, Neil A. Martin, Paul Vespa

Bibliographic record

VenueJournal of Neurotrauma · 2007
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineTraumatic brain injuryGlasgow Outcome ScaleElectroencephalographyNeuroimagingInternal medicineGlasgow Coma ScaleProspective cohort studyWhite matterAnesthesiaMagnetic resonance imagingRadiologyPsychiatry

Abstract

fetched live from OpenAlex

Continuous electroencephalography (cEEG) is potentially useful in determining prognosis in patients with traumatic brain injuries (TBI). The objective of this prospective, observational cohort study was to determine if the percent alpha variability (PAV) on cEEG was predictive of outcome following TBI. Injury characteristics were indexed to assess whether lesions in specific cerebral loci were correlated with PAV and patient recovery. Fifty-three TBI patients were studied using cEEG recording and serial neuroimaging. Clinical recovery was assessed at regular intervals in hospital and following discharge. The principal outcome measures included the mean 3-day PAV score, the 7-day PAV pattern, delineation of the anatomical sites of brain injury, and the 6-month clinical outcome, as measured by the Glasgow Outcome Scale (GOS). Significant univariate (p = 0.030) and multivariate (p = 0.008) relations were identified between PAV and GOS scores. PAV offered good discrimination between favorable and unfavorable 6-month outcomes (AUC 0.76) and, with a cutpoint of 0.20, had a sensitivity of 87% and negative predictive value of 82%. Multivariate modeling revealed that injuries of the thalamus (p = 0.009) and basal ganglia (p = 0.016), and the presence of diffuse edema (p = 0.009), were the key anatomical predictors of PAV. Brainstem injuries (p = 0.020) and indicators of diffuse cerebral trauma, such as deep white matter shearing (p = 0.036) and multiple subcortical lesions (p = 0.033), were the principal determinants of 6-month recovery. Inclusion of PAV enhanced the accuracy of prediction models that encompassed a selective combination of clinical and anatomical variables (adjusted R(2) = 0.458, p < 0.001). The two main results of this study are (1) PAV is a sensitive predictor of 6-month clinical outcomes following TBI, and (2) injury to the thalamus is related to impaired PAV. PAV appears best utilized as a functional adjunct to traditional clinical and anatomical predictors.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.032
GPT teacher head0.313
Teacher spread0.281 · 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

Citations90
Published2007
Admission routes1
Has abstractyes

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