Cognitive, Environmental, and Linguistic Predictors of Syntax in Fragile X Syndrome and Down Syndrome
Bibliographic record
Abstract
PURPOSE: To examine which cognitive, environmental, and speech-language variables predict expressive syntax in boys with fragile X syndrome (FXS), boys with Down syndrome (DS), and typically developing (TD) boys, and whether predictive relationships differed by group. METHOD: We obtained Index of Productive Syntax ( Scarborough, 1990) scores for 18 boys with FXS only, 20 boys with both FXS and an autism spectrum disorder, 27 boys with DS, and 25 younger TD boys of similar nonverbal mental age. Predictors included group (diagnosis), nonverbal cognition, phonological working memory (PWM), maternal education, speech intelligibility, and expressive vocabulary. The research questions were addressed via hierarchical linear regression. RESULTS: Diagnostic group, nonverbal cognition, and PWM predicted 56% of the variance in syntactic ability, with approximately three-fourths of the predicted variance explained by group membership alone. The other factors did not contribute any additional significant variance in this final model. There was no evidence that predictor effects differed by group. CONCLUSIONS: Nonverbal cognition and PWM have an effect on expressive syntax beyond that of diagnostic group. These effects are estimated to be the same in boys with FXS, boys with DS, and TD boys. Explanations for residual variance and the relative role of different predictors are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".