Road traffic injury in Lebanon: A prospective study to assess injury characteristics and risk factors
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".