Validation of a Capillary Electrophoresis Assay for Monitoring Iodine Nutrition in Populations for Prevention of Iodine Deficiency: An Interlaboratory Method Comparison
Bibliographic record
Abstract
BACKGROUND: A capillary electrophoresis (CE) assay was recently introduced as a new method for monitoring iodine nutrition in large-scale epidemiological studies. However, further tests revealed unanticipated matrix-dependent interferences when analyzing submicromolar levels of iodide in human urine as the predominate ionic form of dietary iodine. Herein, we describe a rigorous validation study that was used to identify sources of bias and establish modifications to the original CE method to improve method accuracy. METHODS: An interlaboratory method comparison using CE with UV detection and inductively coupled plasma-mass spectrometry (ICP-MS) was performed to quantify urinary iodide concentrations (n = 71) independently at McMaster University and Hamilton General Hospital, as well as the CDC as part of their quality assurance program. A positive bias in the original CE method was indicated, and buffer conditions were subsequently optimized to overcome matrix interferences for reliable iodine status determination. RESULTS: Positive bias in CE was attributed to variable concentrations of sulfate, a major urinary anion interference with similar mobility to iodide under the conditions originally reported. By increasing the concentration of α-cyclodextrin in the background electrolyte, the CE method was able to tolerate urinary sulfate over its normal physiological range without loss in signal response for iodide. The optimized CE assay generated results that were consistent with ICP-MS using 2 different internal standards (187Re and 130Te) with a median bias under 10%. CONCLUSIONS: CE offers a simple, selective, and cost-effective separation platform for surveillance of the iodine status of a population requiring only small volumes (<10 μL) of biobanked urine specimens, which is comparable to previously validated screening methods currently used in global health initiatives for prevention of iodine deficiency disorders.
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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 | 0.028 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".