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Record W3008203275 · doi:10.1111/gbb.12647

Using a mouse model to gain insights into developmental coordination disorder

2020· review· en· W3008203275 on OpenAlexafffund
Kamaldeep Gill, Jeffy Rajan Soundara Rajan, Dan Goldowitz, Jill G. Zwicker

Bibliographic record

VenueGenes Brain & Behavior · 2020
Typereview
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsSunny Hill Health Centre for ChildrenBC Children's HospitalUniversity of British Columbia
FundersSunny Hill FoundationBC Children's HospitalMichael Smith Health Research BCCanadian Child Health Clinician Scientist ProgramCanadian Institutes of Health ResearchFaculty of Medicine, University of British Columbia
KeywordsAutism spectrum disorderMotor coordinationPsychologyNeurodevelopmental disorderSet (abstract data type)AutismCognitive psychologyConstruct (python library)Developmental psychologyNeuroscienceComputer science

Abstract

fetched live from OpenAlex

Motor impairments are a common feature of many neurodevelopmental disorders; in fact, over 50% of children with Attentional Deficit Hyperactivity Disorder or Autism Spectrum Disorder may have a co-occurring diagnosis of developmental coordination disorder (DCD). DCD is a neurodevelopmental disorder of unknown etiology that affects motor coordination and learning, significantly impacting a child's ability to carry out everyday activities. Animal models play an important role in scientific investigation of behaviour and the mechanisms and processes that are involved in control of motor actions. The purpose of this paper is to present an approach in the mouse directed to gain behavioral and genetic insights into DCD that is designed with high face validity, construct validity and predictive validity. Pre-clinical and clinical expertise is used to establish a set of scientific criteria that the model will meet in order to investigate the potential underlying causes of DCD.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.088
GPT teacher head0.371
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations12
Published2020
Admission routes2
Has abstractyes

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