Studies of age‐correlated features of cognitive‐behavioral development in children and adolescents with genetic disorders
Bibliographic record
Abstract
Studies of age-related features of cognitive-behavioral deficits produced by genetic mutations permit us to draw inferences about how brain development may be related cognitive ability as the child ages. Except for Down syndrome (DS) and the fragile X mutation (FRAXA), little is known about the longitudinal changes in cognitive-behavioral development in individuals with genetic abnormalities producing learning disabilities (LD) or mental retardation (MR). The purpose of this prospective study was to compare and contrast age related to cognitive abilities, adaptive and maladaptive behaviors in children and adolescents in the same age range, diagnosed with one of three genetic disorders: the FRAXA mutation, Neurofibromatosis type 1 (NF1) or Williams-Beuren syndrome (WBS). We also sought to examine whether cognitive-behavioral abilities associated with these three genetic disorders were related systematically to age. We examined 108 children, ages 4-15 years, with FRAXA, WBS, or NF1. Results show that there is a significant negative correlation between age and IQ, and between age and adaptive behavior (DQ) scores, in children with FRAXA and WBS, but not in children with NF1. All three groups of children have unusually high proportions of maladaptive behavior, ranging from 1/6 children with NF1 to 2/3 children with FRAXA. Cognitive and adaptive behavior profiles of children with FRAXA and WBS were also surprisingly similar. Our findings suggest the need for examining longitudinal developmental cognitive-behavioral changes in children and adolescents with all genetic disorders that produce LD or MR.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".