Subway-related trauma at a level 1 trauma centre in Toronto, Ontario
Bibliographic record
Abstract
BACKGROUND: Given the rising prevalence of subways in combination with an increasing incidence of subway-related injuries, understanding subway-related trauma is becoming ever more relevant. The aim of this study was to characterize the potential causes, injury characteristics and outcomes of subway-related trauma at a level 1 adult trauma centre in Toronto, Ontario. METHODS: E-codes (X81, Y02, V050, V051 and W17). We then further screened for descriptions of subway-related injuries. Patients whose injuries did not involve a moving subway train were excluded. RESULTS: We identified 51 patients who presented to the emergency department after being hit by a moving subway train. The majority of incidents (39 [76%]) were due to self-harm, 10 (20%) were unintentional injuries, and 2 (4%) were due to assault. The presence of alcohol was detected in 8 patients (80%) with unintentional injuries and 3 (8%) of those with self-inflicted injuries. Thirteen patients (25%) had a systolic blood pressure less than 90 mm Hg. The median Injury Severity Score was 17 (interquartile range 9-29). Seventeen patients (33%) presented with severe injuries (Abbreviated Injury Scale score ≥ 3) in 1 body region, and 19 (37%) had severe injuries in 2 or more body regions. The most common isolated severe injury was in the lower extremity, and the most common combinations of severe injuries were in the head and lower extremity, and head and thorax. Ten patients (20%) were declared dead in the emergency department. Of the 41 patients who survived their initial presentation, 12 (29%) went directly to the operating room, and 17 (41%) were transferred to the intensive care unit. The overall mortality rate was 29%. CONCLUSION: Patients with subway-related injuries experienced high mortality rates and severe injuries. Most incidents were due to self-harm or alcohol-related. Further research into early identification of those at risk and optimal prevention strategies is necessary to curb further incidents.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".