Risk of Mortality in Association with Pregnancy in Women Following Motor Vehicle Crashes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The aim of the study was to provide a systematic review and meta-analysis of studies examining the association between mortality risk and motor vehicle crashes (MVCs) in pregnant women compared with nonpregnant women. We used relevant MeSH terms to identify epidemiological studies of mortality risk in relation to MVCs from PubMed, Embase, and MEDLINE databases. The Newcastle–Ottawa Scale (NOS) was used for quality assessment. For comparison of mortality from MVCs between pregnant and nonpregnant women, the pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated using a random effects model. The eight studies selected met all inclusion criteria. These studies included 14,120 injured victims who were pregnant at the time of the incident and 207,935 victims who were not pregnant. Compared with nonpregnant women, pregnant women had a moderate but insignificant decrease in mortality risk (pooled OR = 0.68, 95% CI = 0.38–1.22, I2 = 88.71%). Subgroup analysis revealed that the pooled OR significantly increased at 1.64 (95% CI = 1.16–2.33, I2 < 0.01%) for two studies with a similar difference in the mean injury severity score (ISS) between pregnant and nonpregnant women. Future studies should further explore the risk factors associated with MVCs in pregnant women to reduce maternal mortality.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".