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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".