Structural Equation Modelling in the exploration and analysis of intrauterine environmental exposures with infant health effects
Bibliographic record
Abstract
INTRODUCTION: In epidemiology, generalized linear models are the main statistical methods used to explore associations. However, the use of other methods such as Structural Equation Modelling (SEM) is gradually increasing. OBJECTIVE: The aim of the study was to illustrate the use of SEM in the assessment of salivary cortisol concentration in infants as a biomarker of perinatal exposure to inorganic arsenic. MATERIAL AND METHODS: This was a cohort study of pregnant women recruited from public health care centres in Arica, Chile, in 2013. Socio-demographic information and urine samples to assess inorganic arsenic were collected during the second trimester of pregnancy. Saliva samples were collected to assess cortisol in infants between 18-24 months of age. Four linear regression models (LRMs) and two SEMs were run to estimate the effect of prenatal exposure to inorganic arsenic on cortisol concentration in infants. RESULTS: According to LRMs and SEMs, prenatal exposure to inorganic arsenic and salivary cortisol were not associated. However, the association between maternal cortisol and cortisol in infants was statistically significant in all models; for each increase in standard deviation of the covariate Ln(maternal cortisol), the outcome Ln(cortisol in infant) increased by 0.49 units of variance in both SEMs. CONCLUSIONS: LRMs and SEMs were useful to assess the effect of prenatal exposure to inorganic arsenic on cortisol in infants. However, SEM allowed the adjustment of estimations by an estimated latent that obtained the information about income, occupation, education and ethnicity in a more comprehensive way than achieved by LRM.
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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 | 0.033 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".