Is deviant motor development in early childhood a significant predictor for Developmental Coordination Disorder?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".