Does Obesity Increase the Risk of Injury or Mortality in Motor Vehicle Crashes? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The objective of this review was to assess the risk of obesity in injuries and fatalities resulting from motor vehicle crash (MVC), as compared with individuals with a normal-range body mass index. A systematic review of the literature was conducted yielding 824 potential studies. Nine of these studies met our inclusion criteria. Meta-analyses examining obesity as a risk factor for various injury types and risk of fatality were conducted using data from these studies. Obesity was associated with higher fatality risk (odds ratio [OR] = 1.89, 95% confidence interval [CI] = 1.51-2.37, P = .0001; pooled estimate from 6 studies), and increased risk of lower extremity fractures (OR = 1.39, 95% CI = 1.18-1.65, P = .0001; pooled estimate from 2 studies). No significant differences were observed when considering abdominal injuries or pelvic fractures. Interestingly, for head injuries obesity was a protective factor (OR = 0.67, 95% CI = 0.46-0.97, P = .0001; pooled data from 3 studies). Evidence strongly supports the association of obesity with higher fatality and fractures of the lower extremities in MVCs. Contrary to our hypothesis, 3 studies showed that obesity was a protective factor in reducing head injuries. Furthermore, the review shows that obesity was not a risk factor of MVC-related pelvic fractures and abdominal injuries.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".