Urinary iodine concentration identifies pregnant women as iodine deficient yet school-aged children as iodine sufficient in rural Niger
Bibliographic record
Abstract
OBJECTIVE: To assess iodine status among pregnant women in rural Zinder, Niger and to compare their status with the iodine status of school-aged children from the same households. DESIGN: Seventy-three villages in the catchment area of sixteen health centres were randomly selected to participate in the cross-sectional survey. SETTING: Salt iodization is mandatory in Niger, requiring 20-60 ppm iodine at the retail level. SUBJECTS: A spot urine sample was collected from randomly selected pregnant women (n 662) and one school-aged child from the same household (n 373). Urinary iodine concentration (UIC) was assessed as an indicator of iodine status in both groups. Dried blood spots (DBS) were collected from venous blood samples of pregnant women and thyroglobulin (Tg), thyroid-stimulating hormone and total thyroxine were measured. Iodine content of household salt samples (n 108) was assessed by titration. RESULTS: Median iodine content of salt samples was 5·5 ppm (range 0-41 ppm), 98 % had an iodine content 40 µg/l. CONCLUSIONS: In this region of Niger, most salt is inadequately iodized. UIC in pregnant women indicated iodine deficiency, whereas UIC of school-aged children indicated marginally adequate iodine status. Thus, estimating population iodine status based solely on monitoring of UIC among school-aged children may underestimate the risk of iodine deficiency in pregnant women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".