Motor vehicle collision‐related accidents in pregnancy
Bibliographic record
Abstract
AIM: Motor vehicle accidents (MVA) are a major contributor of worldwide morbidity and mortality; however, relatively little is known about the incidence and consequences of traffic accidents on pregnant women. Our aim is to compare rates and outcomes of motor vehicle collision-related accidents in pregnant women. MATERIAL AND METHODS: We conducted a population-based retrospective cohort study using the Healthcare Cost and Utilization Project Nationwide Inpatient Sample database from 2003 to 2011. The risk of different MVA and injuries were compared among pregnant and non-pregnant subjects using conditional logistic regression. RESULTS: We identified 5936 cases of collision-related MVA in pregnancy and age-matched them at a 1:10 ratio to 59,360 non-pregnant women with collision-related MVA. As compared to non-pregnant women, pregnant women who were admitted after an MVA suffered less severe injuries and consequently required fewer therapeutic interventions and a shorter hospital stay. Pregnant women who had a collision-related MVA were, however, at increased risk of requiring genitourinary surgery (odds ratio [OR], 1.45; 95% confidence interval [CI], 1.24-1.69). When restricted to women with a fracture, pregnant women were even more likely to require genitourinary surgery (OR, 2.93; 95%CI, 2.32-3.71) as well as require a blood transfusion (OR, 1.21; 95%CI, 1.01-1.44). CONCLUSION: Pregnant women admitted to hospital after a collision-related MVA tend to sustain less severe injuries compared to non-pregnant women. However, the influence of admissions for fetal monitoring, rather than maternal injury, could not be determined from our dataset. Pregnant women who experienced a collision-related MVA also required less surgical intervention, with the exception of genitourinary surgery, which may be indicative of more cesarean deliveries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".