Using latent profile analysis to examine associations between gestational chemical mixtures and child neurodevelopment
Bibliographic record
Abstract
In this study, we introduced Latent Profile Analysis (LPA) as a novel technique for studying gestational chemical mixtures.Using data from the Maternal-Infant Research on Environmental Chemicals Study, a longitudinal birth cohort study of pregnant Canadian women and their children, we examined the relationship between 30 gestational biomarkers and Verbal IQ, Performance IQ, and Full-Scale IQ.We generated five latent profiles: A Reference profile, a High Level profile, a Low Level profile, a High Organophosphate Pesticides profile, and a Smoking Chemicals profile.Multiple regression analysis showed strong negative associations between the Smoking Chemicals profile and IQ scores.We also found positive associations between the Low Level profile and IQ, and a negative association between the High Level profile and Verbal IQ.However, all 95% confidence intervals spanned the null.After conducting sensitivity analysis comparing LPA with k-means clustering, we concluded that LPA is a promising alternative to other clustering methods.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".