The Accuracy of Potassium Content on Food Labels in Canada
Bibliographic record
Abstract
Background/Objectives: In 2017, the Canadian Government updated labeling requirements for prepackaged products to include potassium as a mandatory nutrient. Higher potassium intakes are beneficial in the general population, but for those with hyperkalemia, a lower potassium intake is recommended. Methods: The Canadian Food Inspection Agency (CFIA) collects food products and analyzes them to determine their potassium content. The authors requested data collected by the CFIA between January 2005 and November 2023 through an Access to Information request (A-2023-00410). Paired-sample two-sided t-tests were used to compare the difference between the labeled and analyzed potassium contents. Cohen’s Kappa was also used to assess agreement between values. Results: Data were available for 406 food items, with 376 having a labeled and analyzed potassium value. The number of samples within each product type was not equally spread; 60% of samples (243/405) were considered dairy analogs—comprising either plant-based milk or cheese products. The mean difference between analyzed and labeled potassium content was statistically significant at 15 mg per serving (SD, 68 mg; 95% CI, 8–22 mg; p < 0.001). Cohen’s Kappa suggested moderate agreement between labeled and analyzed values (κ = 0.376; 95% CI, 0.305–0.447; p < 0.001). A total of 271 (69.7%) products exceeded a ±10% difference, with 90 (23.9%) over-reporting potassium and 181 (48.1%) under-reporting potassium. Conclusions: The total number of products that were compliance-tested for potassium in Canada was relatively low and skewed disproportionately toward plant-based dairy analogs. Most products had labeled potassium values that differed from the lab-analyzed values, with a greater tendency to under-report vs. over-report potassium content. This suggests that at least some labels may not be accurate enough to correctly identify high-potassium foods for those who are following a low-potassium diet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".