Bibliographic record
Abstract
BACKGROUND: Violence and traumatic brain injury (TBI) are two major public health concerns. This violence-related TBI, however, has not been studied in Canada and there is little literature on a profile of risk factors and injury severity regarding TBI because of self-inflicted/suicidal and assault/homicidal injury compared with unintentional TBI. METHODS: Cross-sectional study using the comprehensive data set (CDS) of Ontario trauma registry (OTR) from 1993 to 2001. SETTING: Data from a large trauma registry were used to assess demographic and injury-related characteristics, injury severity and incident time associated with TBI that were either caused unintentionally, were self-inflicted or resulted from an assault. RESULTS: This study identified 1,409 (8.0%) intentional TBIs and 16,211 (92.0%) unintentional TBIs. Of the intentional TBIs, 389 (27.6%) was self-inflicted TBI (Si-TBI) and 1,020 (72.4%) was other-inflicted TBI (Oi-TBI). The most common cause of Si-TBI was "jumping from high places" (32.1%), followed by "firearms" (30.6%). About half of Oi-TBI was because of "fight and brawl" (48.3%), followed by "struck by objects" (26.1%). Si-TBI was associated with younger age, female gender, and having more alcohol/drug abuse history. For Oi-TBI, younger age, male gender, having more alcohol/drug abuse history were independently associated. CONCLUSION: This research provides the first comprehensive overview of intentional TBI based on Canadian data. The CDS of the OTR provided the ability to identify who is at risk for intentional TBI. Prevention programs and more targeted rehabilitation services should be designed for this vulnerable 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".