Factors associated with road traffic injury severity among victims retrieved by pre-hospital emergency services in Rwanda
Bibliographic record
Abstract
Background: Traumatic injuries remain critical public health concerns, placing psychosocial and economic burdens on individuals, families, and healthcare systems. Despite being a leading cause of morbidity and mortality globally, especially in low-and middle-income countries, few studies have examined predictors of injury severity in pre-hospital settings. Most research focuses on injury incidence, with limited attention to pre-hospital factors. We aimed assessing the prevalence of severe injuries and their associated factors among patients managed by pre-hospital emergency services. Methods: This cross-sectional study utilized medical registry data from 1,162 RTI victims. Demographic, epidemiological, and clinical information were collected, with injuries categorized as severe or non-severe based on the Injury Severity Score. Bivariate and multivariable logistic regression models were conducted to indicate associated factors of severe injury. Results: Among 1162 victims, 165 (14%) experienced severe injury. Our results showed that females were less likely to experience severe injury (aOR=0.47, 95%CI:0.26-0.79) than males. Regarding trauma mechanism, car-to-pedestrian collisions (aOR=2.3, 95%CI:1.25-4.1), car-to-motorcycle collisions considerably increased the likelihoods of severe injury (aOR=3.88, 95%CI:1.16-13.05) compared to car-only crashes. Alcohol users were more likely to experience severe injury (aOR=3.37; 95%CI: 2.04-5.56) than non-users. Those who travelled distance ranged 21-40 km had higher likelihoods (aOR=2.91, 95%CI:1.27-6.63), while those with more than 40 km faced higher likelihoods of severe injury (aOR=2.64, 95% CI:1.11-6.25) than individuals with less than 20 km to reach to a healthcare facility. Those with extremity injuries (aOR=0.28, 95% CI:0.15-0.52), chest injuries (aOR=0.40, 95%CI:0.23-0.71, p=.002) had lower likelihoods of severe injury than those with head trauma. Conclusion: This study provides valuable insights into the factors influencing injury severity in the pre-hospital setting. The findings underscore the importance of strengthening early identification and rapid stabilization of high-risk patients during pre-hospital care. Future research using prospective longitudinal designs is recommended to confirm causality.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".