PW 2578 Sentinel surveillance of injuries related to substance use and abuse among the canadian pediatric population
Bibliographic record
Abstract
Substance use is an individual behaviour that is also embedded in sociocultural context. The use of such psychoactive agents has been associated with injuries, most notably poisoning. This study seeks to identify and describe injuries related to substance use in the Canadian pediatric population that were captured within the electronic Canadian Hospitals Injury Reporting and Prevention Program (eCHIRPP) database. Records in the eCHIRPP system, years 2011 to 2016, were extracted for ages below 18 years old. Existing variable codes and narrative text were used to identify injuries related to substance use (depressants, stimulants, opioids, cannabis, and alcohol). Descriptive statistics (intent, type of injury, and hospitalization) were calculated using SAS Enterprise Guide version 5.1 and are presented. A total of 3363 cases of injury related to substance use were identified, with a frequency of 627 per 1 00 000 eCHIRPP cases. Among cases where substance use was identified, poly-substance use was observed in 14.9% of cases, and the use of depressants only in 2.4% of cases, stimulants only in 1.0% of cases, opioids in 3.2% of cases, cannabis in 12.7% of cases, and alcohol in 65.9% of cases. Poisoning accounted for most injuries (72.0%), and the majority of injuries were unintentional in nature (67.1%), although cases of physical assault (12.8%), self-harm (11.3%), and sexual assault (0.8%) were observed. Injuries severe enough to warrant hospitalization occurred in 12.1% of cases. Injuries related to the use of different categories of substances have been identified in pediatric populations presenting to eCHIRPP centres. Given the possibility of physical and psychological dependence on psychoactive agents, continued surveillance and awareness health literacy efforts are warranted. Continued surveillance and monitoring of injuries related to substance use should help to inform future injury prevention efforts.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".