Contribution of Substance Use in Acute Injuries With Regards to the Intent, Nature and Context of Injury: A CHIRPP Database Study
Bibliographic record
Abstract
Introduction Using the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) sentinel surveillance system, the objective of this study was to compare intent, circumstances, injury type and patient demographics in patients who used a substance prior to the injury versus those who did not use any substances. Methods Data were retrospectively collected from November 1st 2016 to October 31st 2017. All patients presenting to the Hôpital de l’Enfant-Jésus ED following trauma were included, aside from those who left without seeing a physician or had no physical injury (e.g., overdose without any trauma was excluded). Patients voluntarily completed a standardised form or agreed to be contacted later. Medical charts of all attendances were reviewed by the CHIRPP’s program coordinator. Substance use included illicit drugs, medications for recreational purposes, alcohol or other used either by the patient or another person involved. Results A total of 12,857 patients were included. Substance use was involved in 701 (5.5%) cases and was associated with injuries sustained by males (p < .001). The mean age of patients injured while using substances was 42.8 years, compared to 45.5 years in those who did not use substances (p < .001). Substance use was involved in 3.6% of unintentional injuries, compared to 26.2% of injuries intentionally inflicted by other and 38.9% for self-inflicted injuries (p < 0.0001). When substances were used, the odds of intentional injuries were 7.5 times greater compared to non-intentional injuries (95% CI 6.7, 8.5). Burns, head injuries and polytraumas were more prevalent when drugs or alcohol were involved. Conclusion This study outlines the significant contribution of substance use in intentional injuries, suggesting that it could potentially be beneficial to specifically target patients who present with deliberate physical injuries in preventive and therapeutic interventions offered in the ED.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".