64 E-cigarette and Vaporizer-related Burn Injury: Demographics and Injury Patterns
Bibliographic record
Abstract
Abstract Introduction E-cigarettes and vaporizers (E-cigs) have seen a dramatic surge in popularity. The Centers for Disease Control now estimates that 1 in 4 United States (US) high school students use E-cigs. Recent recognition of E-cig-related lung injury has garnered significant attention. However, relatively little is known about E-cig-related burn injuries, which are often due to battery explosion and liquid ignition. The objective of this study was to characterize population demographics and injury patterns associated with E-cig burns. Methods A retrospective review of all patients presenting with E-cig-related cutaneous burn injury to a verified US burn center between January 2015 and August 2019 was performed using an institutional database. In addition, media reports covering the same mechanism of injury and date range were collected using databases for the National Electronic Injury Surveillance System, American Nonsmokers’ Rights Foundation, and international news outlets. Demographic data and injury metrics were recorded. Results 309 international media reports and 30 institutional cases of E-cig-related burn injuries were identified. Media-reported injuries varied with respect to geographic location: US-185, United Kingdom-35, Canada-6, all other countries-8, unclear-75. Annual incidence peaked in 2016 in media reports (2019-19, 2018–61, 2017–68, 2016–106, 2015–55) and institutional records (2019-3, 2018-5, 2017-6, 2016-16, 2015-0). Injuries predominantly involved men in both media (Male 75.7%, Female 13.9%, unknown 10.4%) and institutional (Male 93.3%, Female 6.7%) datasets. Average age was 31.0 years (media) and 30.3 years (institutional). The institutional data revealed an average TBSA of 3.5% (range 1–8.5%), an average length of stay of 3.8 days (range 0–14 days), and a median number of operations of 1 (range 0–2). 90% (n=27) of patients required inpatient admission and one patient required mechanical ventilation. On admission, 58% (n=14) of patients screened positive for cannabis use. A ranking of anatomic regions by frequency of involvement was consistent between datasets: lower extremity > hands > head/neck > torso > groin. Mortality rates were also similar: 1.62% (media) and 0% (institutional). Conclusions E-cig-related burns are non-trivial injuries, most of which require inpatient admission, operative management, and substantial resource use. They disproportionately affect young men and burns often involve sensitive areas like the hands and face. Applicability of Research to Practice Combined with rising popularity, lax regulation, proliferation of counterfeit products, and associated lung injury, E-cig-related burns represent an evolving health threat. This study highlights the importance of reporting injuries to consumer product regulatory agencies and the need for further research into the causes and consequences of E-cig explosion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".