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Record W2101790232 · doi:10.1109/tbme.2010.2090880

Towards Brain First-Aid: A Diagnostic Device for Conscious Awareness

2010· article· en· W2101790232 on OpenAlexafffund
Ryan C.N. D’Arcy, Sujoy Ghosh Hajra, C Liu, Lauren D. Sculthorpe, Donald F. Weaver

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2010
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsQueen Elizabeth II Health Sciences CentreNational Research Council CanadaNational Research Council Institute for BiodiagnosticsDalhousie University
FundersNational Research Council Canada
KeywordsComputer scienceElectroencephalographyReliability (semiconductor)SoftwareVariety (cybernetics)PerceptionBrain activity and meditationHuman–computer interactionArtificial intelligencePsychologyNeuroscience

Abstract

fetched live from OpenAlex

When the brain is damaged, evaluating an individual's level of awareness can be a major diagnostic challenge (Is he or she in there?). Existing tests typically rely on behavioral indicators, which are incorrect in as many as one out of every two cases. The current paper presents a diagnostic device that addresses this problem. The technology circumvents behavioral limitations through noninvasive brain wave measurements (electroencephalography, or EEG). Unlike traditional EEG, the device is designed for point-of-care use by incorporating a portable, user-friendly, and stable design. It uses a novel software algorithm that automates subject stimulation, data acquisition/analysis, and the reporting of results. The test provides indicators for five identifiable levels of neural processing: sensation, perception, attention, memory, and language. The results are provided as rapidly obtained diagnostic, reliability, validity, and prognostic scores. The device can be applied to a wide variety of patients across a host of different environments. The technology is designed to be wireless-enabled for remote monitoring and assessment capabilities. In essence, the device is developed to scan for conscious awareness in order to optimize subsequent patient care.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.793
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.270
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations26
Published2010
Admission routes2
Has abstractyes

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