Assessment of Nicotine delivery capabilities and evaluation of human health risk of metals associated with selected tobacco products
Bibliographic record
Abstract
The use of smokeless tobacco has increased, particularly among Ghana's youth, due in part to perceived medicinal benefits and the belief that it is less harmful and non-addictive. In response to this trend, we systematically classified 51 different tobacco products based on their potential toxicity as determined by potentially toxic elements, addiction potential, and nicotine delivery capability. Moisture, pH, and percentage-free base nicotine were measured using certified methods. X-ray fluorescence techniques were used to determine tobacco products' concentrations of target elements (Ca, Cu, Fe, K, Mn, Rb, Sr, Mo, V, S, U, Zr, Tl, and Zn). Locally produced snuff products had the highest nicotine delivery capacity, with a pH of 9.73 and 96.98% freebase nicotine. Dried tobacco leaves followed closely, with a mean pH of 7.39 and percentage free base nicotine of 25.93%, whereas cigarette products had the lowest nicotine delivery capability (mean pH: 5.49; percentage free base nicotine: 0.33%). In the snuff category, menthol-flavored products delivered more nicotine (pH of 9.96; percentage free base nicotine of 98.8%) than moringa-flavored alternatives (pH of 9.77; percentage free base nicotine of 98%). Users of the smuggled cigarette product C6 were found to be susceptible to increased non-carcinogenic health effects, as indicated by a hazard index (HI) value of 1108.35, while C4, with the highest pH of 5.58 and a corresponding %A (addiction potential) of 0.70, demonstrated the greatest addiction potential among the examined cigarette products. Our findings indicate that locally produced snuff and dried tobacco leaves have potentially high addictive properties, emphasizing the risk of tobacco dependence. Furthermore, these products may have non-carcinogenic health effects, as indicated by elevated hazard quotients and hazard indices. These findings provide important insights into the various characteristics of tobacco products in Ghana, which may aid in developing targeted public health interventions.
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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 | 0.002 | 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) | 0.001 | 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".