Association Between Maternal Iodine Intake in Pregnancy and Childhood Neurodevelopment at Age 18 Months
Bibliographic record
Abstract
There are limited and inconsistent data suggesting that mild iodine deficiency in pregnancy might be associated with poorer developmental outcomes in children. Between 2011 and 2015, we conducted a prospective cohort study in Australia examining the relationship between maternal iodine intake in pregnancy and childhood neurodevelopment, assessed using Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III), in 699 children at 18 months. Maternal iodine intake and urinary iodine concentration (UIC) were assessed at study entry (<20 weeks' gestation) and at 28 weeks' gestation. Maternal iodine intake in the lowest (<220 μg/day) or highest (≥391 μg/day) quartile was associated with lower cognitive, language, and motor scores (mean differences ranged from 2.4 (95% confidence interval (CI): 0.01, 4.8) to 7.0 (95% CI: 2.8, 11.1) points lower) and higher odds (odds ratios ranged from 2.7 (95% CI: 1.3, 5.6) to 2.8 (95% CI: 1.3, 5.7)) of cognitive developmental delay (Bayley-III score <1 SD) compared with mothers with an iodine intake in the middle quartiles. There was no association between UIC in pregnancy and Bayley-III outcomes regardless of whether UIC and the outcomes were analyzed as continuous or categorical variables. Both low and high iodine intakes in pregnancy were associated with poorer childhood neurodevelopment in this iodine-sufficient population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".