Comparison of breast-milk iodine concentration of lactating women in Australia pre and post mandatory iodine fortification
Bibliographic record
Abstract
OBJECTIVE: To compare the breast-milk iodine concentrations (BMIC) of lactating women before and after the mandatory iodine fortification of bread in Australia in 2009. DESIGN: Cross-sectional study. Breast milk samples were collected from two cohorts of women in South Australia within 7 d of delivery to determine BMIC. The percentage of samples with iodine concentration below 100 µg/l, a level considered adequate for breast-fed infants, was calculated. Sociodemographic information and intake of dietary supplements were obtained from all women. SETTING: The breast milk samples were collected between 2006 and 2007 in the pre-fortification cohort and between 2012 and 2013 in the post-fortification cohort. RESULTS: The median (interquartile range) BMIC was higher in the post-fortification samples compared with samples collected in the pre-fortification period (187 (130-276) v. 103 (73-156) µg/l; P<0·05). Overall, the percentage of women with BMIC <100 µg/l was lower in the post-fortification cohort than in the pre-fortification cohort (13 v. 49 %; P<0·01). The percentage of women with BMIC <100 µg/l in the post-fortification cohort was lower among women who took iodine supplements in pregnancy (12 v. 29 %; P<0·01). CONCLUSIONS: Mandatory iodine fortification of bread has resulted in an increase in the iodine content of breast milk in Australian women. However, iodine supplementation may still be required in some women post-iodine fortification to reach the level of BMIC that is considered adequate to meet the iodine requirement of full-term infants.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".