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Record W2078991161 · doi:10.3109/02699052.2014.916819

Spectral power, source localization and microstates to quantify chronic deficits from ‘mild’ closed head injury: Correlation with classic neuropsychological tests

2014· article· en· W2078991161 on OpenAlexaff
Paula L. Corradini, Michael A. Persinger

Bibliographic record

VenueBrain Injury · 2014
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsLaurentian University
Fundersnot available
KeywordsNeuropsychologyMinistateClosed head injuryPsychologyAudiologyNeuropsychological assessmentTraumatic brain injuryHead injuryPoison controlPhysical medicine and rehabilitationElectroencephalographyNeuroscienceCognitionPsychiatryMedicine

Abstract

fetched live from OpenAlex

PRIMARY OBJECTIVE: To explore the quantitative relationship between neuropsychological impairment and spectral QEEG power, source localization (s-LORETA) and microstate duration in patients who 'fail to adapt' years after mild closed head injury. METHODS AND PROCEDURES: Differences in classic psychometric measures, QEEG measures, s-LORETA indicators and microstate durations were compared for three levels of neuropsychological impairment ∼(average) 6 years after injury. RESULTS: Patients who displayed the moderate-to-severe neuropsychological impairments typical of mild TBIs exhibited shorter microstates, less power within the alpha band and lower power within the theta and delta bands within caudal regions. There were conspicuous differences in the configurations of microstates, their source localizations and frequency bands. CONCLUSIONS: The systematic relationship between neuropsychological impairment as inferred by classic psychometric measures that can require tens of hours to collect and modern configurational analyses of brain activity that require ∼30 minutes suggests that the latter might be useful in discerning the area of dysfunction and potential focus of treatment for patients with closed head injury years after the event.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
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
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.340
Teacher spread0.303 · 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 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

Citations19
Published2014
Admission routes1
Has abstractyes

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