Fast Colorimetric Method for Measuring Urinary Iodine
Bibliographic record
Abstract
International groups recommend the following median urinary iodine concentration as the best single indicator of iodine nutrition in populations: severe deficiency, 0–0.15 μmol/L (0–19 μg/L); moderate deficiency, 0.16–0.38 μmol/L (20–49 μg/L); mild deficiency, 0.40–0.78 μmol/L (50–99 μg/L); optimal iodine nutrition, 0.79–1.56 μmol/L (100–199 μg/L); more than adequate iodine intake, 1.57–2.36 μmol/L (200–299 μg/L); and excessive iodine intake, ≥2.37 μmol/L (≥300 μg/L) (1). The range in which the median falls is more important than the precise number (2)(3). Many methods for assessing urinary iodine exist (3)(4)(5)(6)(7)(8), most based on the Sandell–Kolthoff reaction (9), in which iodide catalyzes the reduction of ceric ammonium sulfate (yellow) to the colorless cerous form in the presence of arsenious acid. Although iodide is the chemical form for both the catalytic reaction and in urine, some preliminary treatment is needed to rid urine of impurities, most commonly by acid digestion (3)(5). We have extended previous approaches (5)(6)(10) with improved conditions and here present a new method (“Fast B”) that is rapid, inexpensive, reliable, and flexible.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".