PA 10-3-2582 Intentional trauma: emergency department presentations for traumatic brain injuries associated with intentional injury at all ages
Bibliographic record
Abstract
Background Injuries that occur as a result of purposeful human action to cause harm directed either to oneself or to another is referred to as an intentional injury. Objective To identify and describe the detailed mechanisms of cases of traumatic brain injuries (TBI) related to non-fatal intentional injuries that were captured within the electronic Canadian Hospitals Injury Reporting and Prevention Program (eCHIRPP) database. Methods Records in the eCHIRPP system between April 1, 2011 and July 17, 2017 were extracted for all ages. TBIs were identified based on nature of injury and body part codes. Descriptive statistics and text mining (PERL regular expressions) were conducted using SAS Enterprise Guide version 5.1. Findings A total of 2273 cases of TBI were identified from the eCHIRPP database (12 591 cases per 1 00 000 intentional injury eCHIRPP cases (all injuries)). Between 2011 and 2017, the frequency of intentional TBI cases, and intentional TBI cases attributable to assault, have been decreasing significantly (p<0.001 for both). However, the frequency of intentional TBI cases attributable to self-harm have remained stable (p=0.92). The majority of cases occurred among those 15 to 19 years old. Physical assault was the most common mechanism of TBI (79.3%), but instances of child maltreatment (6.6%), self-harm without use of drugs (4.5%), and abusive head trauma (0.5%) were among other mechanisms identified. Almost a quarter of intentional TBIs were admitted to hospital (24021/100,000 intentional TBI cases). Conclusions Intentional trauma occurs at every age, with physical assault as the most likely mechanism. A large proportion of intentional injuries that result in TBIs are hospitalized. While it’s promising that intentional TBI cases appear to be on the decline, the stability of intentional self-harm TBI cases is of concern. Policy implications Continued surveillance and monitoring of intentional injuries should help to inform our understanding of such injuries in Canada.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.273 | 0.093 |
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".