Health risks of potentially toxic metals in cereal-based breakfast meals in the Kumasi Metropolis, Ghana
Bibliographic record
Abstract
Abstract Metal exposure through cereal-based breakfasts was studied in humans of varied ages (infants, toddlers, children, adolescents, adults, and the elderly) to assess the potential health risks metals in food pose to consumers. The total concentrations of As (0.59–0.69 mg/kg), Cd (1.27–1.41 mg/kg), Cr (4.66–9.85 mg/kg), Mn (8.26–9.73 mg/kg), Ni (5.01–5.81 mg/kg) and Pb (0.83–0.92 mg/kg) were all higher than the regulatory limits for metals in cereal-based foods. Metal concentrations assessed via in-vitro bioaccessibility extracts were below the detection limits. Toxicity indices of As, Cd, Cr, Ni, and Pb were above their respective WHO-permissible tolerable daily intake for all age groups, implying possible health risks due to over-exposure to metals. While the hazard quotients for Cr and Mn among the age groups were less than 1, those for Cd and Ni were greater than 1. There was no public health concern for cancer risk associated with oral exposure to Pb among the various age groups. However, the estimated cancer risk of Cd (185.4 × 10–3) and As (9.2 × 10–3) was greater than the de minimus (10–6), suggesting a public health concern among various age groups. The study found a significant level of metal contaminants in cereal-based foods, which can potentially pose health risks to consumers who consume them.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".