Brief Report: Lead Levels in Selected Electronic Cigarettes from Canada and the United States
Bibliographic record
Abstract
Few published studies have investigated the presence of lead in the e-liquid of electronic nicotine delivery systems (ENDS). Lead inhalation is associated with increased risk of stroke, heart disease, and other diseases. This study used a novel application of graphite furnace technology to compare the concentration of lead between e-liquids of different packaging and product designs using e-liquids that are or were commercially available in the United States and Canada. Eleven nicotine-free disposable ENDS devices and 12 bottled refill solutions that contained nicotine were purchased from retailers in Canada and the United States between 2015 and 2017. E-liquids extracted from the disposable products and individual containers were analyzed for lead content by graphite furnace using atomic absorption detection. The lead concentration of open-wick ENDS devices ranged from 25.2 ppb to 838.4 ppb, with a standard deviation of 187.4 ppb. None of the bottled e-liquids contained quantifiable levels of lead. This study found that quantifiable levels of lead are present in certain disposable e-cigarette devices, and there is evidence from this study that the design of ENDS devices may contribute to lead exposure. These findings suggest that lead testing should be incorporated into future chemical analyses of ENDS devices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".