Sodium intake status in United States and potential reduction modeling: an <scp>NHANES</scp> 2007–2010 analysis
Bibliographic record
Abstract
Limiting dietary sodium intake has been a consistent dietary recommendation. Using NHANES 2007-2010 data, we estimated current sodium intake and modeled the potential impact of a new sodium reduction technology on sodium intake. NHANES 2007-2010 data were used to assess current sodium intake. The National Cancer Institute method was used for usual intake determination. Suggested sodium reductions using SODA-LO (®) Salt Microspheres ranged from 20% to 30% in 953 foods and usual intakes were modeled by using various reduction factors and levels of market penetration. SAS 9.2, SUDAAN 11, and NHANES survey weights were used in all calculations with assessment across gender and age groups. Current (2007-2010) sodium intake (mg/day) exceeds recommendations across all age gender groups and has not changed during the last decade. However, sodium intake measured as a function of food intake (mg/g food) has decreased significantly during the last decade. Two food categories contribute about 2/3rd of total sodium intake: "Grain Products" and "Meat, Poultry, Fish & Mixtures". Sodium reduction, with 100% market penetration of the new technology, was estimated to be 230-300 mg/day or 7-9% of intake depending upon age and gender group. Sodium reduction innovations like SODA-LO (®) Salt Microspheres could contribute to meaningful reductions in sodium intake.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".