Patterns of injuries and injury severity among hospitalized road traffic injury (RTI) patients in Bangladesh
Bibliographic record
Abstract
BACKGROUND: Road traffic injuries (RTIs) are one of the key public health issues worldwide causing 1.3 million deaths every year. This study aimed to determine the patterns of injuries due to road traffic accidents (RTAs), the severity of injuries, and factors associated with injury severity. METHODOLOGY: A cross-sectional study was conducted among RTA victims, who attended two large tertiary care hospitals located inside the Dhaka metropolitan area, through structured interview between 28 January and 22 March 2020. RESULTS: Among 375 RTI patients, a total of 1390 injuries were recorded among interviewed patients, yielding a mean of 3.7 injuries per patient. The most frequently injured systems were external (n = 351), lower limb (n = 235), head and neck (n = 151), and face (n = 150). The mean ISS were 20.96 ± 12.027 with a maximum of 65 and a minimum of 4. Among patients, 87 (23.20%) had a severe injury, and 37 (9.87%) patients were critically injured. A statistically significant variation in ISS was observed in ANOVA among various categories of age, education, occupation, and purpose of going outside, vehicle type and fitness, accident type, road type, times required in hospitalization, and death history (p < 0.05). CONCLUSIONS: Our study has revealed several important findings which will help stakeholders and policymakers devise better policies to reduce RTA and RTA related injuries in Bangladesh.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".