Electronic cigarettes
Bibliographic record
Abstract

 Background: Since 2011, the popularity of electronic cigarettes in North America has increased dramatically. However, with a lack of scientific data performed on long term health effects and the limited number of short term studies, it is difficult for Environmental Health Officers to effectively educate the public on concerns relating to the health and safety of the general public. The increase of teenage users demonstrates the need for better government legislation and enforcement, in order to prevent the re-glamorization of smoking in younger generations. Therefore, the following study conducted a chemical analysis on artificially inhaled vapor from two different types of e-cigarettes (disposable and rechargeable), to determine if any heavy metal concentrations; specifically cadmium, chromium, lead and arsenic, are detectable. Methods: The vapor from one of two e-cigarette types was artificially inhaled through a cellulose filter cassette by a personal sampling pump. A two tailed t-test was performed to determine if there were any differences between the heavy metals and the type of e-cigarette used in the study. Results: There was no statistical significant difference in heavy metal concentration by the type of e-cigarette used (for cadmium the p-value was 0.00, and power was 0.00, for chromium the p-value was 0.181220, and power was 0.008976342, for lead the p-value was 0.333711, and power was 0.001825742, for arsenic the p-value was 0.00, and power was 0.00). Conclusion: Based on the results, it was determined that there was no statistical significance between disposable e-cigarettes and rechargeable e-cigarettes with respect to concentration of the four heavy metals of interest (eg. cadmium, chromium, lead and arsenic). Although there was no statistical significance between the types of e-cigarettes used, the average concentration of chromium (IV) from the rechargeable e-cigarette was 0.13mg/m3, which is ten times the recommended 8-hour time weighted average (TWA) set by the BC Occupational Health and Safety Regulations. Hence, further studies must be conducted to determine if the average concentration found in this study truly reflects the concentration found in inhaled vapor from rechargeable e-cigarettes. Furthermore, environmental health officers can provide the public with the concentration found in this study and warn of potential health risks associated with e-cigarettes until further studies are released.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".