Iodine deficiency among pregnant women living in Northern Ireland
Bibliographic record
Abstract
OBJECTIVE: Mild iodine deficiency has re-emerged among school girls in the UK. We wished to study a contemporaneous pregnant population because a relationship between maternal iodine deficiency and offspring cognitive scores has recently been reported. The WHO has set a median population urinary iodine concentration (UIC) of ≥100 and ≥150 µg/L to define adequacy outside of and during pregnancy, respectively. Iodine creatinine ratio (ICR) is also used to correct for dilution effects (sufficiency ≥150 µg/g creatinine in pregnancy). DESIGN AND METHODS: A total of 241 women were followed across trimesters (T) into the postpartum period (PPP) along with 80 offspring with spot urine sampling and food frequency questionnaires. RESULTS: Median UIC was 73 µg/L in the 1st T (ICR 102 µg/g creatinine) despite 55% taking iodine-containing supplements. Median UICs were 94, 117 and 90 µg/L in the 2nd T, 3rd T and PPP, respectively. Corresponding ICRs were 120, 126 and 60 µg/g creatinine. ICR was associated with volume of milk consumed throughout pregnancy. Median UIC among the offspring was 148 µg/L, with no difference between the breast- and formula-fed babies. CONCLUSIONS: Pregnant women living in Northern Ireland may be at risk of iodine deficiency across pregnancy and into the PPP while the offspring are iodine sufficient. This is the first study of its kind in the UK with data for pregnant women and their offspring. The UK does not provide an iodine fortification programme nor offer routine iodine dietary advice in pregnancy and this requires consideration by public health agencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".