Strengthening surveillance of consumer products in Canada: the vaping example
Bibliographic record
Abstract
INTRODUCTION: The overall objective of this study was to demonstrate how information collected by the Consumer Product Safety Program ("the Program") can be used to identify emerging hazards. Specifically, this study characterized and quantified trends associated with vaping reports received by the Program over the past five years. METHODS: Data collated by the Program were extracted for the period from 1 January, 2015 to 30 September, 2019. The data were summarized using descriptive statistics and trends were quantified for annual percent change. In order to compare characteristics of vaping reports, the proportionate injury ratios (PIRs) and corresponding 95% CIs were used to compare vaping-related injuries to all other reports received by the Program. RESULTS: A total of 71 vaping-related reports were received between 1 January, 2015 and 30 September, 2019. During this period, the annual percent change increase in the number of reports received was approximately 73% annually (p < .05). Among the reported injuries, 41% were burn injuries. Proportionally, there were more vaping reports involving males (PIR = 1.89; 95% CI: 1.51-2.36) and individuals between the ages of 15 and 19 years (PIR = 11.53; 95 % CI: 4.95-26.8) as compared to all other reports submitted to the Program. CONCLUSIONS: While the number of reports relating to vaping products is small, the results of this analysis suggest that certain groups, including males and youth, are more likely to be the subject of a vaping-related incident.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".