Risk and Protective Factors for Late Talking: An Epidemiologic Investigation
Bibliographic record
Abstract
OBJECTIVE: To identify risk and protective factors for late talking in toddlers between 24 and 30 months of age in a large community-based cohort. STUDY DESIGN: A prospective, longitudinal pregnancy cohort of 1023 mother-infant pairs in metropolitan Calgary, Canada, were followed across 5 time points: before 25 weeks gestation, between 34-36 weeks gestation, and at 4, 12, and 24 months postpartum. Toddlers who scored ≤10th percentile on The MacArthur-Bates Communicative Development Inventories: Words and Sentences between 24 and 30 months of age were identified as late talkers. Thirty-four candidate characteristics theoretically and/or empirically linked to language development and/or language impairment were collected using survey methodology. RESULTS: The prevalence of late talking was 12.6%. Risk factors for late talking in the multivariable model included: male sex (P = .017) and a family history of late talking and/or diagnosed speech or language delay (P = .002). Toddlers were significantly less likely to be late talkers if they engaged in informal play opportunities (P = .013), were read to or shown picture books daily (P < .001), or cared for primarily in child care centers (P = .001). CONCLUSIONS: Both biological and environmental factors were associated with the development of late talking. Biological factors placed toddlers at risk for late talking, and facets of the environment played a protective role. Enveloping infants and toddlers in language-rich milieus that promote opportunities for playing, reading, and sharing books daily may decrease risk for delayed early vocabulary.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".