Reliability of thyroglobulin in serum compared with urinary iodine when assessing individual and population iodine nutrition status
Bibliographic record
Abstract
The occurrence of thyroid disorders relies on I nutrition and monitoring of all populations is recommended. Measuring I in urine is standard but thyroglobulin in serum is an alternative. This led us to assess the reliability of studies using serum thyroglobulin compared with urinary I to assess the I nutrition level and calculate the number of participants needed in a study with repeated data sampling in the same individuals for 1 year. Diet, supplement use and life style factors were assessed by questionnaires. We measured thyroglobulin and thyroglobulin antibodies in serum and I in urine. Participants were thirty-three Caucasians and sixty-four Inuit living in Greenland aged 30-49 years. Serum thyroglobulin decreased with rising I excretion (Kendall's τ -0·29, P=0·005) and did not differ with ethnicity. Variation in individuals was lower for serum-thyroglobulin than for urinary I (mean individual CV: 15·1 v. 46·1 %; P<0·01). It required 245 urine samples to be 95 % certain of having a urinary I excretion within 10 % of the true mean of the population. For serum-thyroglobulin the same precision required 206 samples. In an individual ten times more samples were needed to depict I deficiency when using urinary I excretion compared with serum-thyroglobulin. In conclusion, more participants are need to portray I deficiency in a population when using urinary I compared with serum-thyroglobulin, and about ten times more samples are needed in an individual. Adding serum-thyroglobulin to urinary I may inform surveys of I nutrition by allowing subgroup analysis with similar reliability.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".