Misclassification of iodine intake level from morning spot urine samples with high iodine excretion among Inuit and non-Inuit in Greenland
Bibliographic record
Abstract
Iodine nutrition is commonly assessed from iodine excretion in urine. A 24 h urine sample is ideal, but it is cumbersome and inconvenient. Hence, spot urine samples with creatinine to adjust for differences in void volume are widely used. Still, the importance of ethnicity and the timing of spot urine samples need to be settled. We, thus, collected 104 early morning spot urine samples and 24 h urine samples from Inuit and non-Inuit living in Greenland. Diet was assessed by a FFQ. Demographic data were collected from the national registry and by questionnaires. Iodine was measured using the Sandell-Kolthoff reaction, creatinine using the Jaffe method and para-amino benzoic acid by the HPLC method for the estimation of completeness of urine sampling and compensation of incomplete urine samples to 24 h excretion. A population-based recruitment was done from the capital city, a major town and a settlement (n 36/48/20). Participants were seventy-eight Inuit and twenty-six non-Inuit. The median 24 h iodine excretion was 138 (25th-75th percentile 89-225) μg/97 (25th-75th percentile 72-124) μg in Inuit/non-Inuit (P= 0.030), and 153 (25th-75th percentile 97-251) μg/102 (25th-75th percentile 73-138) μg (P= 0.026) when including compensated iodine excretion. Iodine excretion in 24 h urine samples increased with a rising intake of traditional Inuit foods (P= 0.005). Iodine excretion was lower in morning spot urine samples than in 24 h urine samples (P< 0.001). This difference was associated with iodine intake levels (P< 0.001), and was statistically significant when the iodine excretion level was above 150 μg/24 h. In conclusion, the iodine intake level was underestimated from morning spot urine samples if iodine excretion was above the recommended level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".