Chemistry Review of Vaping Products and Respiratory Injury
Bibliographic record
Abstract
Background: While the Public Health Agency of Canada notes 19 cases from May 2019 to February 2020 relating to e-cigarette or vaping product use-associated lung injury (EVALI) in Canada, there are likely many more unreported cases, including non-hospitalized and asymptomatic cases. E-cigarette use or vaping exposes users to numerous aerosolized chemical species, some of which have proven to be deleterious to health. These chemical species can include vitamin E acetate (VEA), flavourants, base / solvents (propylene glycol or vegetable glycerin), psychoactive substances, pesticides, endotoxins, metals, and pyrolysis by-products from e-cigarette heating coils. Objectives: We aim to review current findings related to EVALI from the standpoint of known chemical species currently used in vaping products. We specifically examine the toxicological profiles of these chemical species and the mechanisms through which they cause lung injury. Methods: A comprehensive literature search was performed with MEDLINE for EVALI-related human studies that were published between January 1, 2010, and May 15, 2020. This search strategy identified 832 case reports, case series, clinical trials, and in-vitro laboratory studies. From this group, 71 records were examined in greater detail. Results and Conclusions: Although the chemical composition and toxicology of vaping products have largely been characterized, the physiological effects of the chemical interactions between various constituents of vaping products and the generation of new species remain inconclusive. Given the rapid increase in the popularity of vaping and e-cigarettes, there is a need for further research. Developing a comprehensive understanding of the chronic health effects of vaping through randomized controlled trials and physiological studies is prudent and necessary to reduce the long-term impacts on users and the health care system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".