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Record W2737566996

Intra-limb coordination in boys with and without Developmental Coordination Disorder (DCD)

2011· article· en· W2737566996 on OpenAlexaff
Michael J. Asmussen, Eryk Przysucha, Carlos Zerpa

Bibliographic record

VenueJournal of Exercise, Movement, and Sport · 2011
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsLakehead University
Fundersnot available
KeywordsContext (archaeology)Motor coordinationElbowPhysical medicine and rehabilitationEye–hand coordinationWristPsychologyPhysical therapyMathematicsMedicineSurgeryGeography
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to determine differences in the nature and effectiveness of intra-limb coordination in boys with and without DCD, in one-handed catching. Ten boys, in each group, attempted to catch at ten balls at 7m/s. Two high-speed cameras (100Hz) were used to derive 3D coordinates. The degree of coordination was inferred from the magnitude of correlation coefficient between angular displacement of the relevant joints; higher values indicated tighter coupling. Standard deviation, calculated across five trials, was used to infer stability. Results showed a significant interaction effect ( F =10.64, p 2 = .39) for the degree of spatial coupling, and subsequent planned comparisons revealed significant differences between the groups for shoulder-elbow (Sh-El) ( w/o DCD = .69; DCD = .80) and elbow-wrist (El-Wr) ( w/o DCD = .79; DCD = .61) joint pairs. Also, a significant group main effect was found for stability ( F (1, 18) = 7.97, p 2 = .32) for the El-Wr ( w/o DCD = .11; DCD = .21). In terms of effectiveness, boys w/ DCD caught fewer balls ( w/o DCD = 85%; DCD = 32%). Contrary to previous work, no universal coordinative tendency emerged for either group confirming that (intra-limb) coordination is dependent on many different constraints. The potential causes of failures exhibited by DCD will be discussed in the context of relevant models of redundancy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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.

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
Published2011
Admission routes1
Has abstractyes

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