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Record W3158272291 · doi:10.24908/iqurcp.7746

Multi-Joint Movement Coordination in Children with Fetal Alcohol Spectrum Disorders

2017· article· en· W3158272291 on OpenAlexvenueno aff
Dilmini Peiris

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2017
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsFetal alcoholElbowMotor coordinationPhysical medicine and rehabilitationFetal alcohol syndromeFetal Alcohol Spectrum DisorderMedicineAlcohol use disorderPsychologyAlcoholPhysical therapyPsychiatryPregnancySurgery

Abstract

fetched live from OpenAlex

The purpose of this study is to examine how human subjects with central nervous system injury resulting from prenatal exposure to alcohol coordinate multi-joint movements involving the shoulder and elbow joints. The study focuses on children and young adults (1) with a diagnosis of a Fetal Alcohol Spectrum Disorder (FASD), which is an umbrella term used to define Fetal Alcohol Syndrome, Partial Fetal Alcohol Syndrome, Alcohol Related Neurodevelopmental Disorder, and Alcohol Related Birth Defects; and (2) age- and gender-matched control subjects. The subjects are given a series of simple coordination tasks to perform, on a robot exoskeleton called the KINARM and the experimental data collected by the soft ware will be analyzed in great detail. The overall goal of this research project is to determine, whether motor movements and control of the limbs can be used as a potential diagnostic and research tool in children with developmental disorders.

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)0.001
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.089
GPT teacher head0.368
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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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Same venueInquiry Queen s Undergraduate Research Conference ProceedingsSame topicCerebral Palsy and Movement DisordersFrench-language works237,207