MétaCan
Menu
Back to cohort
Record W2013134536 · doi:10.1017/s0317167100013998

A New Method of Intracranial Pressure Monitoring by EEG Power Spectrum Analysis

2012· article· en· W2013134536 on OpenAlexvenueno aff
Hui Chen, Jian Wang, Mao Sizhong, Weiwei Dong, Hao Yang

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2012
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsnot available
Fundersnot available
KeywordsElectroencephalographyIntracranial pressureLumbar punctureSpectrum analysisSpectral densityMedicineSpectral analysisAnesthesiaBiomedical engineeringInternal medicineMathematicsCerebrospinal fluidStatisticsPhysicsSpectroscopy

Abstract

fetched live from OpenAlex

OBJECTIVES: To investigate the feasibility of Electroencephalogram (EEG) power spectrum analysis as a noninvasive method for monitoring intracranial pressure (ICP). METHODS: The EEG signals were recorded in 62 patients (70 cases) with central nervous system (CNS) disorders in our hospital. By using self-designed software, EEG power spectrum analysis was conducted and pressure index (PI) was calculated automatically. Intracranial pressure was measured by lumbar puncture (LP). RESULTS: We found a significant negative correlation between PI and ICP (r = -0.849, p < 0.01). CONCLUSIONS: The PI obtained from EEG analysis is correlated with ICP. Analysis of specific parameters from EEG power spectrum might reflect the ICP.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.302
Teacher spread0.274 · 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 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

Citations41
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicTraumatic Brain Injury and Neurovascular DisturbancesFrench-language works237,207