Median Urinary Iodine Concentrations Are Indicative of Adequate Iodine Status among Women of Reproductive Age in Prey Veng, Cambodia
Bibliographic record
Abstract
Iodine deficiency disorders are estimated to affect over 1.9 million people worldwide. Iodine deficiency is especially serious for women during pregnancy and lactation because of the negative consequences for both mother and infant. The aim of this cross-sectional study was to determine the median urinary iodine concentration (UIC) as a population-level indicator of iodine status among rural women farmers of reproductive age (18-45 years) in the province of Prey Veng, Cambodia. A total of 450 women provided a spot morning urine sample in 2012. Of those women, 93% (n = 420) were non-pregnant and 7% (n = 30) were pregnant at the time of collection. UIC was quantified using the Sandell-Kolthoff reaction with modifications. The median UIC of non-pregnant (139 μg/L) and pregnant women (157 μg/L) were indicative of adequate iodine status using the WHO/UNICEF/ICCIDD epidemiological criteria for both groups (median UIC between 100-199 and 150-249 μg/L, respectively). We conclude that non-pregnant and pregnant women in rural Prey Veng, Cambodia had adequate iodine status based on single spot morning urine samples collected in 2012. More research is warranted to investigate iodine status among larger and more representative populations of women in Cambodia, especially in light of recent policy changes to the national program for universal salt iodization.
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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 | 0.001 |
| 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) | 0.002 | 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".