Systematic review of studies evaluating urinary iodine concentration as a predictor of 24-hour urinary iodine excretion for estimating population iodine intake.
Bibliographic record
Abstract
OBJECTIVE: To examine the usefulness of "spot" urine iodine concentrations (UICs) in predicting 24-hour urine iodine excretion (UIE) for estimating average population iodine intake. METHODS: An electronic literature search was conducted for articles published through 19 May 2013 in MEDLINE (from 1950), EMBASE (from 1980), and the Cochrane Library (from 1993) using the terms "urinary excretion (timed or spot or random) and (24 h or 24 hour), iodine (iodine deficiency), iodine (intake)," and "urine (timed, spot, random, 24-hour)." Full-text articles about studies that examined > 40 healthy human subjects and measured UIE using the 24-hour urine collection method and UIC and/or UIE using one alternative method (spot (random), timed, and "overnight" (first morning urine), fasting or not fasting) were selected and reviewed. RESULTS: The review included data from 1 434 participants across the six studies that met the inclusion criteria. The main statistical methods for comparing data from the 24-hour urine collections with the values obtained from the alternative method(s) were either regression (β) or correlation (r) coefficients and concordance analysis through Bland-Altman plots. The urine samples collected using the alternative methods were subject to greater intra-individual and inter-individual variability than the 24-hour urine collections. There was a wide range in coefficient values for the comparisons between 24-hour URE measured in 24-hour urine collection and 24-hour UIE estimated using the alternative sampling methods. No alternative sampling method (spot, timed, or "overnight") was appropriate for estimating 24-hour UIE. CONCLUSIONS: The results of this systematic review suggest current data on UICs as a means of predicting 24-hour UIE for estimating population sodium intake are inadequate and highlight the need for further methodological investigations.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".