Developmental Coordination Disorder before the Age of Three: A Longitudinal Retrospective Study in a Belgian Center for Developmental Disabilities
Bibliographic record
Abstract
This study aimed to explore the association between developmental coordination disorder (DCD) diagnosed after the age of three and both a standardized motor test—the Alberta Infant Motor Scale (AIMS)—and non-standardized observation of movement quality carried out before the age of three. Children at risk or with developmental concerns were studied retrospectively. Children were excluded in case of a diagnosis, excluding DCD, e.g., cerebral palsy, or IQ < 70. Of the 503 included children, 246 were diagnosed with (at-risk) DCD. Multivariate binary logistic regression revealed a significant association between DCD diagnosis after the age of three and male gender and with different aspects of poor movement quality in different age groups before the age three. Univariate analyses revealed an association between DCD diagnosis and the number of poor movement-quality descriptions at 0−6 months, 6−12 months, and 18 months−3 years but not with the AIMS scores. The MABC-2 scores after the age of three were significantly correlated with the number of poor movement-quality descriptions in age groups 0−6 months and 18 months−3 years and with the AIMS scores in age groups 6−12 months and 12−18 months. The results suggest that DCD can be associated with poor movement quality before the age of three.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".