Acute injury or illness related to the inhalation of vaping aerosols among children and adolescents across Canada: A cross-sectional survey of Canadian paediatricians
Bibliographic record
Abstract
Background: Vaping prevalence rates have increased among Canadian youth. Evidence suggests that vaping poses significant health risks to children and adolescents. Objectives: The objectives of the study were to investigate epidemiological characteristics of acute injury/illness cases due to the inhalation of vaping aerosols among children and adolescents across Canada and to explore factors contributing to severe cases. Methods: Data from the 2019 Canadian Paediatric Surveillance Program cross-sectional survey on vaping-related injury/illness were used. Analyses focused on injury/illness cases (n=71) among children and adolescents aged 0 to 17 years who presented to participating paediatricians for a harm related to the inhalation of vaping aerosols. We conducted descriptive analyses and performed logistic regression to explore associations between severe presentations requiring hospitalization or intensive care unit (ICU) admission and selected case characteristics. Results: Of the 71 reported injury/illness cases related to inhalation of vaping aerosols, 56% of patients were male, and 68% were aged 15 to 17 years. Nicotine vaping was reported in 42% of cases, and cannabis vaping in 24%. Fifty-four per cent presented with respiratory distress, 18% with symptoms of nicotine toxicity, and 41% required hospitalization and/or admission to the ICU. Cases presenting with respiratory distress were more likely to be hospitalized/admitted to the ICU (odds ratio [OR]=5.37, 95% confidence interval [CI]:1.76 to 16.39). Conclusions: The inhalation of vaping aerosols among children and adolescents may contribute to acute injury/illness. Clear associations between study variables and severe cases could not be established due to a small sample size. Additional research is needed to determine predictors and preventable risk factors of severe vaping-related injuries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".