Opportunities and challenges in capturing severe vaping-related injuries among Canadian children and youth
Bibliographic record
Abstract
Although the long-term harms associated with vaping remain largely unknown, there have been numerous accounts of acute vaping-related injuries in the paediatric population. The study of vaping-related injuries is an important yet challenging undertaking, complicated by a lack of appropriate reporting mechanisms and the absence of consensus on definitions and diagnostic codes. We discuss the results of a 12-month national cross-sectional study from the Canadian Paediatric Surveillance Program conducted in 2021-2022 and situate these results within the broader context of other Canadian surveillance and reporting mechanisms. Fewer than five cases of vaping-associated injuries were reported, contrasting with previous surveys which had revealed much higher case numbers. Hypotheses for the low case numbers include decreased exposure to vaping in the context of COVID-19, changes in vaping products, increases in public awareness of vaping-related harms, as well as recent modifications in policies related to vaping product marketing and sales. There is a great need for a multi-pronged surveillance approach leveraging multiple data sources, including self-reported provider and consumer data, as well as administrative data to help inform clinicians and policymakers on how to prevent vaping-associated injuries among youth.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".