Developing a Web-based dietary sodium screening tool for personalized assessment and feedback
Bibliographic record
Abstract
Dietary sodium reduction is commonly used in the treatment of hypertension, heart and liver failure, and chronic kidney disease. Sodium reduction is also an important public health problem since most of the Canadian population consumes sodium in excess of their daily requirements. Lack of awareness about the amount of sodium consumed and the sources of sodium in diet is common, and undoubtedly a major contributor to excess sodium consumption. There are few known tools available to screen and provide personalized information about sodium in the diet. Therefore, we developed a Web-based sodium intake screening tool called the Salt Calculator ( www.projectbiglife.ca ), which is publicly available for individuals to assess the amount and sources of sodium in their diet. The Calculator contains 23 questions focusing on restaurant foods, packaged foods, and added salt. Questions were developed using sodium consumption data from the Canadian Community Health Survey cycle 2.2 and up-to-date information on sodium levels in packaged and restaurant food databases from the University of Toronto. The Calculator translates existing knowledge about dietary sodium into a tool that can be accessed by the public as well as integrated into clinical practice to address the high levels of sodium presently in the Canadian diet.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".