Assessment of salt intake to consider salt as a fortification vehicle for thiamine in Cambodia
Bibliographic record
Abstract
Thiamine deficiency is a public health issue in Cambodia. Thiamine fortification of salt has been proposed; however, the salt intake of lactating women, the target population, is currently unknown. We estimated salt intakes among lactating women (<6 months postpartum) using three methods: repeat observed-weighed intake records and 24-h urinary sodium excretions (n = 104), and household salt disappearance (n = 331). Usual salt intake was estimated by adjusting for intraindividual intakes using the National Cancer Institute method, and a thiamine salt fortification scenario was modeled using a modified estimated average requirement (EAR) cut-point method. Unadjusted salt intake from observed intakes was 9.3 (8.3-10.3) g/day, which was not different from estimated salt intake from urinary sodium excretions, 9.0 (8.4-9.7) g/day (P = 0.3). Estimated salt use from household salt disappearance was 11.3 (10.7-11.9) g/person/day. Usual (adjusted) salt intake from all sources was 7.7 (7.4-8.0) g/day. Assuming no stability losses, a modeled fortification dose of 275 mg thiamine/kg salt could increase thiamine intakes from fortified salt to 2.1 (2.0-2.2) mg/day, with even low salt consumers reaching the EAR of 1.2 mg/day from fortified salt alone. These findings, in conjunction with future sensory and stability research, can inform a potential salt fortification program in Cambodia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".