Road traffic injury in Lebanon: A prospective study to assess injury characteristics and risk factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Road traffic injury (RTI) is a significant yet poorly characterized cause of morbidity and mortality in the Middle East. This hospital‐based‐study examined RTI in Lebanon and provided an understanding of their characteristics. Methods We collected prospective RTI data from three participating hospitals over 3 months using a designed tool based on Canadian CHIRPP and WHO tools. We performed logistic regression analysis to examine the relationship between contributing risk factors (age, sex) and injury types as well as the association of safety measures used (seatbelts or helmets) and body parts injured. Results A total of 153 patients were collected. Male preponderance with 72%, with mean age 32.6 (SD = 14.9) years. RTI was highest among passengers aged 15 to 29 (48%). Motorcyclists comprised the greatest injury proportion (38%), followed by vehicle‐occupants (35%), and pedestrians (25%) ( P = .04). Hip injuries represented the most affected body part (48.7%), followed by head/neck (38.2%). Only 31% (n = 47) of victims applied safety measures (seatbelts or helmets). Six drivers (7%) reported cell phone use at collision. The use of safety measures was associated with a substantial reduction in head/neck injuries ( P = .03), spine injuries ( P = .049), and lower risk of traumatic brain injury (TBI) ( P = .02). Conclusions RTI is a major health problem in Lebanon. Safety measures, though poorly adhered to, were associated with less severe injuries, and should be further promoted via awareness campaigns and enforcement. Trauma registries are needed to assess the RTI burden and inform safety interventions and quality‐of‐care improvement programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it