Profiles and predictors of child neurodevelopment and anthropometry: The maternal-infant research on environmental chemicals study
Bibliographic record
Abstract
Background: Evaluating individual health outcomes does not capture co-morbidities children experience. Purpose: We aimed to describe profiles of child neurodevelopment and anthropometry and identify their predictors. Methods: Using data from 501 mother-child pairs (age 3-years) in the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a prospective cohort study, we developed phenotypic profiles by applying latent profile analysis to twelve neurodevelopmental and anthropometric traits. Using multinomial regression, we evaluated odds of phenotypic profiles based on maternal, sociodemographic, and child level characteristics. Results: For neurodevelopmental outcomes, we identified three profiles characterized by Non-optimal (9%), Typical (49%), and Optimal neurodevelopment (42%). For anthropometric outcomes, we observed three profiles of Low (12%), Average (61%), and Excess Adiposity (27%). When examining joint profiles, few children had both Non-optimal neurodevelopment and Excess Adiposity (2%). Lower household income, lower birthweight, younger gestational age, decreased caregiving environment, greater maternal depressive symptoms, and male sex were associated with increased odds of being in the Non-optimal neurodevelopment profile. Higher pre-pregnancy body mass index was associated with increased odds of being in the Excess Adiposity profile. Conclusions: Phenotypic profiles of child neurodevelopment and adiposity were associated with maternal, sociodemographic, and child level characteristics. Few children had both non-optimal neurodevelopment and excess adiposity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".