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Effect of Multi-site Variabilities on Electrovestibulography: Environmental and Physical Factors

2019· article· en· W2972549727 on OpenAlexaff
Brian Lithgow, Zahra Moussavi

Bibliographic record

VenueResearch and Development on Information and Communication Technology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNeurodegenerationPopulationDiseaseMedicineNeuroscienceAudiologyPsychologyPathologyEnvironmental health

Abstract

fetched live from OpenAlex

Background: There are physiological changes in pathologies such as Alzheimer’s Disease (AD) within the lower vestibuloacoustic system, which may be potentially useful when used as neurodegeneration features. We hypothesize two Electrovestibulography feature types (Field Potential (FP) shape and the Firing pattern of detected FP’s) may have utility as Neurodegeneration features. Our long term objective is to use a population of Parkinson’s Disease (PD), AD, Post-Concussion Syndrome (PCS), Bipolar Disorder (BD) and Major Depressive Disorder (MDD) patients together with individual pathology-wise age and gender matched control cohorts to determine the degree to which each of these pathologies varies from controls and in proportion to the level of Neurodegeneration often associated (either temporarily or permanently) with each pathology. However, before such a comparison can be made it is necessary to ensure the various populations recorded across different countries are comparable. This paper determines which populations are comparable. Methods: An initial comparison of AD (with N = 16) and a best matched healthy control population (with specific age/gender/recording site/electrode matched controls) from a pool of 112 controls produced two EVestG features (FP shape and FP firing pattern). These features were examined for their variability with respect to electrode type, age, gender, powerline frequency and environmental factors. Results: Age and gender did not have a significant impact on the features. Powerline and environmental artefacts could be accounted for by filtering; thus, they did not significantly affect the features measured. However, electrode type had a significant effect on the extracted features. Conclusions: For the two EVestG features tested only electrode type had a significant effect on the recordings, and hence the extracted features. Thus, only populations with the same electrode type can be compared.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.297
Teacher spread0.279 · 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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