Excess iodine intake: sources, assessment, and effects on thyroid function
Bibliographic record
Abstract
Iodine is essential for thyroid hormone synthesis. High iodine intakes are well tolerated by most healthy individuals, but in some people, excess iodine intakes may precipitate hyperthyroidism, hypothyroidism, goiter, and/or thyroid autoimmunity. Individuals with preexisting thyroid disease or those previously exposed to iodine deficiency may be more susceptible to thyroid disorders due to an increase in iodine intake, in some cases at intakes only slightly above physiological needs. Thyroid dysfunction due to excess iodine intake is usually mild and transient, but iodine-induced hyperthyroidism can be life-threatening in some individuals. At the population level, excess iodine intakes may arise from consumption of overiodized salt, drinking water, animal milk rich in iodine, certain seaweeds, iodine-containing dietary supplements, and from a combination of these sources. The median urinary iodine concentration (UIC) of a population reflects the total iodine intake from all sources and can accurately identify populations with excessive iodine intakes. Our review describes the association between excess iodine intake and thyroid function. We outline potential sources of excess iodine intake and the physiological responses and consequences of excess iodine intakes. We provide guidance on choice of biomarkers to assess iodine intake, with an emphasis on the UIC and thyroglobulin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".