Seat Belt Use and its Effect on Abdominal Trauma: A National Trauma Databank Study
Bibliographic record
Abstract
We sought to use the National Trauma Databank to determine the demographics, injury distribution, associated abdominal injuries, and outcomes of those patients who are restrained versus unrestrained. All victims of motor vehicle collisions (MVCs) were identified from the National Trauma Databank and stratified into subpopulations depending on the use of seat belts. A total of 150,161 MVC victims were included in this study, 72,394 (48%) were belted. Young, male passengers were the least likely to be wearing a seat belt. Restrained victims were less likely to have severe injury as measured by Injury Severity Score and Abbreviated Injury Score. Restrained victims were also less likely to suffer solid organ injuries (9.7% vs 12%, P < 0.001), but more likely to have hollow viscous injuries (1.9% vs 1.3%, P < 0.001). The hospital and intensive care unit length of stay were significantly shorter in belted victims with adjusted mean difference: -1.36 (-1.45, -1.27) and -0.96 (-1.02, -0.90), respectively. Seat belt use was associated with a significantly lower crude mortality than unrestrained victims (1.9% vs 3.3%, P < 0.001), and after adjusting for differences in age, gender, position in vehicle, and deployment of air bags, the protective effect remained (adjusted odds ratio for mortality 0.50, 95% confidence interval 0.47, 0.54). In conclusion, MVC victims wearing seat belts have a significant reduction in the severity of injuries in all body areas, lower mortality, a shorter hospital stay, and decreased length of stay in the intensive care unit. The nature of abdominal injuries, however, was significantly different, with a higher incidence of hollow viscous injury in those wearing seat belts.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".