Predicting Language at 2 Years of Age: A Prospective Community Study
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Bibliographic record
Abstract
OBJECTIVE: This article responds to evidence gaps regarding language impairment identified by the US Preventive Services Task Force in 2006. We examine the contributions of putative child, family, and environmental risk factors to language outcomes at 24 months of age. METHODS: A community-ascertained sample of 1720 infants who were recruited at 8 months of age were followed at ages 12 and 24 months in a prospective, longitudinal study in metropolitan Melbourne, Australia. Outcomes at 24 months were parent-reported infant communication (Communication and Symbolic Behavior Scales and MacArthur-Bates Communicative Development Inventories vocabulary production score). Putative risk factors were gender, preterm birth, birth weight, multiple birth, birth order, socioeconomic status, maternal mental health, maternal vocabulary and education, maternal age at birth of child, non-English-speaking background, and family history of speech-language difficulties. Linear regression models were fitted to total standardized Communication and Symbolic Behavior Scales and Communicative Development Inventories vocabulary production scores; a logistic regression model was fitted to late-talking status at 24 months. RESULTS: The regression models accounted for 4.3% and 7.0% of the variation in the 24-month Communication and Symbolic Behavior Scales and Communicative Development Inventories scores, respectively. Male gender and family history were strongly associated with poorer outcomes on both instruments. Lower Communication and Symbolic Behavior Scales scores were also associated with lower maternal vocabulary and older maternal age. Lower vocabulary production scores were associated with birth order and non-English-speaking background. When the 12-month Communication and Symbolic Behavior Scales Total score was added as a covariate in the linear regression of 24-month Communication and Symbolic Behavior Scales Total score, it was by far the strongest predictor. CONCLUSIONS: These early risk factors explained no more than 7% of the variation in language at 24 months. They seem unlikely to be helpful in screening for early language delay.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it