Early identification of children with/at risk of developmental coordination disorder: a scoping review
Bibliographic record
Abstract
AIM: To summarize current evidence for early identification and motor-based intervention for children aged 5 years and younger with/at risk of developmental coordination disorder (DCD). METHOD: Using scoping review methodology and after duplicates were removed, 11 115 peer-reviewed articles and grey literature were independently screened by two authors. Data from 103 included records were extracted and synthesized by both assessors. One author entered the relevant data into tables, while the other author double-checked the entries. RESULTS: Records included peer-reviewed studies, guidelines, conference presentations, and theses/dissertations. Most literature pertained to early identification (n=78), with fewer studies targeting intervention (n=22) or covering both topics (n=3). Literature was summarized in two main categories: (1) assessments for diagnostic criteria A and B; and (2) motor-based interventions for young children with/at risk of DCD. This article highlights the findings related to assessments, while a companion article summarizes the intervention literature. INTERPRETATION: Emerging evidence shows that children, especially those at greatest risk of DCD, may be identified before formal school entry. Earlier identification will allow for earlier intervention, which may help to improve the developmental trajectories of children with/at risk of DCD and prevent secondary consequences of the disorder. It is recommended that health care providers explicitly use the term 'at risk of DCD'.
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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 | 0.016 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".