A Case for Early Screening: Prenatal Alcohol Risk Exposure Predicts Risk for Early Childhood Communication Delays
Bibliographic record
Abstract
OBJECTIVE: Studies have confirmed the detrimental effects of prenatal alcohol exposure on language development in children. Little is known about the ability of prenatal alcohol risk (PAR) screening measures to predict language or other neurodevelopmental delays in young children, however. The intent of this study is to identify whether PAR predicts communication development in children at 12, 24, and 36 months of age. METHOD: Data from 772 women and their children who participated in the All Our Families pregnancy cohort were analyzed. Respondents completed the T-ACE, a validated screening tool for detecting PAR. Communication development in children was measured through the Ages and Stages Questionnaire, Third Edition. Logistic regression was used to generate odds ratios and 95% confidence intervals. RESULTS: A positive screen for PAR places a child at risk for communication delay (≤1 SD below mean) by approximately 1.5-fold at 12, 24, and 36 months of age, even after adjustment for demographic variables. Follow-up analysis revealed a significant difference in the prevalence of risk for communication delays between 12 and 24 months and between 24 and 36 months in both low- and high-risk drinking groups, with 24-month-old children showing the greatest risk for delay. CONCLUSION: The results of this study suggest that screening for PAR in expectant mothers may identify a group of young children at increased risk for communication delays. This research carries clinical implications and provides support for PAR screening in informing early childhood developmental screening efforts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".