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Record W7066751372

Is deviant motor development in early childhood a significant predictor for Developmental Coordination Disorder?

2018· article· en· W7066751372 on OpenAlexaboutno aff

Bibliographic record

VenueGhent University Academic Bibliography (Ghent University) · 2018
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
Fundersnot available
KeywordsMovement assessmentDevelopmental disorderMotor skillAutism spectrum disorderMedical diagnosisHypotoniaPsychomotor learningPervasive developmental disorderDevelopmental age
DOInot available

Abstract

fetched live from OpenAlex

Aim. As EACD guidelines (2012) suggest that developmental coordination disorder (DCD) should not be diagnosed before the age of three, the disorder is most frequently identified after the age of five. As early identification and treatment may help to reduce the emotional, physical and social consequences that are clearly associated with DCD, this study aims to identify possible qualitative markers in early motor development that could be utilized to predict DCD. Method. Participants. Data from 562 children, assessed initially between 2006 and 2011 in the Centre of Developmental Disabilities Ghent (Belgium), were retrospectively selected from the files of 4336 screened children. Inclusion criteria were (1) to be assessed at least once before and once after the age of three; (2) no diagnosis known to have an impact on motor development; (3) an IQ, measured after three years of age, above 70. Procedure. X motor assessments before the age of three were categorized in three age groups (0 - 10 months, 11 - 18 months and 18 months - 3 y). Qualitative descriptions were dichotomously collected in nine different categories: (1) hypotonia or instability, (2) hypertonia, (3) orthopaedic problems, (4) asymmetry, (5) problems with organization and coordination, (6) balance problems, (7) slowness of movement, (8) soft neurological signs, (9) bottom shuffling. Available results of the Alberta Infant Motor Scales (AIMS) were included. Subsequently, reported diagnoses (or at-risk diagnoses) after the age of three of DCD, autism spectrum disorder or attention deficit and hyperactivity disorder were collected. Available results of the Movement Assessment Battery for Children 2 were included. Data analysis. logistic hierarchical regression analysis will be utilized to generate the best possible model to predict DCD by early motor qualitative markers and AIMS scores. Results. Data analysis is ongoing and results will be available at the time of the conference.

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)0.006
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.228
Teacher spread0.211 · 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
Published2018
Admission routes1
Has abstractyes

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