Substance use in paediatric trauma: setting the stage for an injury prevention programme
Bibliographic record
Abstract
Injury prevention initiatives provide education to increase awareness of high-risk behaviours and teach strategies to prevent injury. To be successful, these initiatives must identify at risk populations and understand how factors interact and lead to injury. Substance use is an injury risk factor, however, research is lacking in the paediatric population. This study was undertaken to describe trends in substance use and screening in the paediatric trauma population. A review of the London Health Sciences Centre (LHSC) Trauma Database (1999–2009) was conducted to identify patients <18 years who suffered severe injury (ISS >11). A total of 799 patients met inclusion criteria. Blood alcohol concentration (BAC) testing was completed in 30% of patients of whom 21% were positive. Toxicology screens were done in 7% of patients, of whom 44% were positive. Increasing age was associated with screening for alcohol; while, screening for drug use had a bimodal distribution with no children ages 4–10 tested. Those screened for drugs and alcohol had significantly higher ISS than those not tested (BAC 28 vs 23, p<0.001, toxin screening 29 vs 24, p=0.003). The most common drugs ingested were alcohol, benzodiazepines, cannabioids and opiates. Screening for substance use is sporadic in the paediatric trauma population at LHSC providing incomplete data on its true prevalence and impact. This lack of understanding limits our ability to create effective, age-appropriate injury prevention programmes. A prospective study utilising universal screening is needed to further delineate the true impact of substance use in this young population.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".