Characterisation of vaping liquids used in vaping devices across four countries: results from an analysis of selected vaping liquids reported by users in the 2016 ITC Four Country Smoking and Vaping Survey
Bibliographic record
Abstract
OBJECTIVES: This study presents an analysis of vaping products (VPs) purchased in the USA, Canada, England and Australia and assesses whether differences in regulations were associated with differences in the chemical composition of the VPs. METHODS: April-September 2017, a total of 234 VP refill liquids and prefilled cartridges were purchased in convenience samples of retail locations in each country. Products were chosen from brands and styles most commonly reported by current VP users in the 2016 ITC Four Country Smoking and Vaping Survey. All products were tested for nicotine, tobacco-specific nitrosamines (TSNAs), minor tobacco alkaloids, organic acids and flavouring chemicals. RESULTS: Consistent with the laws in Canada and Australia at the time of product purchase, nicotine was not detected in any of the VPs (n=10 in Canada; n=15 in Australia). US liquids (n=54) had a mean nicotine concentration of 16.2 mg/mL, (range=0.0-58.6) and English liquids (n=166) had a mean concentration of 11.9 mg/mL ((range=0.0-31.2) F(3244)=12.32, p<0.001). About 5% of English samples exceeded the UK's 20 mg/mL nicotine limit. Substantial country differences were observed in levels ofTSNAs, with the USA being higher than elsewhere. Of all products tested, 18.8% contained at least one organic acid. Liquids purchased in England contained far more identifiable flavouring chemicals than those in the other countries. CONCLUSIONS: VP composition, particularly with respect to nicotine and flavouring, varies by country, likely reflecting both marketplace preferences and country-specific regulations. There are differences between nicotine levels claimed on the package and actual levels, particularly in England.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".