Maternal iodine levels and associations with offspring outcomes and growth: a prospective birth cohort study of Chinese pregnant women
Bibliographic record
Abstract
Background: Iodine deficiency during pregnancy has been associated with various adverse outcomes; however, recent data on iodine status among women in Hangzhou, China, remain limited. Methods: Between 2019 and 2022, this birth cohort study enrolled 290 eligible pregnant women at ≤12 weeks of gestation. A standardized, self-developed questionnaire was used to survey each participant, covering demographic information, pregnancy details, dietary iodine intake, and related topics. Physical examination findings of each participant and their offspring were collected. Maternal urinary iodine concentration was measured in the first, second, and last trimesters of pregnancy. Offspring outcomes were evaluated by measuring weight and length at birth, 1 month, 6 months, and 18 months, along with the recording of incidences of spontaneous premature birth, small for gestational age (SGA), and low birth weight. Results: Higher maternal urinary iodine concentration (UIC) during the third trimester was found to be a protective factor against spontaneous premature birth and SGA. Similarly, elevated maternal UIC in the second trimester was identified as a protective factor against spontaneous premature birth. No significant association was observed between low birth weight and maternal UIC in the first, second, and last trimesters. However, offspring born to mothers with iodine insufficiency in the last trimester exhibited lower birth weight and length, as well as reduced length during follow-up. Additionally, offspring of mothers with iodine insufficiency in the first trimester showed lower long-term weight and length. Conclusions: These findings highlight the importance of enhanced monitoring of iodine status in pregnant women to mitigate related adverse outcomes.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".