The Impact of Cognitive Impairment in Children with Intellectual Disabilities
Bibliographic record
Abstract
The focus of the present study is to identify the most compromised aspects of cognitive impairment and how children with intellectual disabilities cope with them. A sample of 53 school aged children with intellectual disabilities (N = 24) female and (N = 29) male from 5 to 11 years old. Montreal Cognitive Assessment scale (MoCA) and Raven’s Standard Progressive Matrices (IQ) to children with intellectual disabilities were used for conducting research. Generally, children with intellectual disabilities experience high and moderate levels of cognitive impairments and a low IQ. There is a significant positive relationship between age and IQ among girls and boys. In addition to cognitive impairment, a positive relation between impaired cognitive function, a high level of global disability in children with intellectual disabilities and a poor executive and memory functions were associated with difficulties in daily life activities. IQ is also a significant index of cognitive impairment and how children interact with others. Cognitive impairment is a major cause of low adoption with the environment and a significant factor that affects rehabilitation outcomes. Yet, there have been a limited number of studies that have evaluated the psychometric MoCA in children with severe intellectual disabilities, but it is necessary to identify possible difficulties in children with ID related to cognitive functions in young patients with mild to moderate impairment.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".