Maternal trauma due to motor vehicle crashes and pregnancy outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To systematically review and quantify the effect of motor vehicle crashes (MVCs) in pregnancy on maternal and offspring outcomes. DESIGN: Systematic review and meta-analysis of observational data searched from inception until 1 July 2018. Searching was from June to August 2018 in Medline, Embase, Web of Science, Scopus, Latin-American and Caribbean System on Health Sciences Information, Scientific Electronic Library Online, TRANSPORT, International Road Research Documentation, European Conference of Ministers of Transportation Databases, Cochrane Database of Systematic Reviews and Cochrane Central Register. PARTICIPANTS: Studies were selected if they focused on the effects of exposure MVC during pregnancy versus non-exposure, with follow-up to verify outcomes in various settings, including secondary care, collision and emergency, and inpatient care. DATA SYNTHESIS: For incidence data, we calculated a pooled estimate per 1000 women. For comparison of outcomes between women involved and those not involved in MVC, we calculated ORs with 95% CIs. Where possible, we statistically pooled the data using the random-effects model. The quality of studies used in the comparative analysis was assessed with Newcastle-Ottawa Scale. RESULTS: =92.6%). Pooled incidence of complications per 1000 women involved in MVC was labour induction (276.43), preterm delivery (191.90) and caesarean section (166.65). Compared with women not involved in MVC, those involved had increased odds of placental abruption (OR 1.43, 95% CI 1.27-1.63; 3 studies, 1 500 825 women) and maternal death (OR 202.27; 95% CI 110.60-369.95; 1 study, 1 094 559 women). CONCLUSION: Pregnant women involved in MVC were at higher risk of maternal death and complications than those not involved. PROSPERO REGISTRATION NUMBER: CRD42018100788.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.009 | 0.009 |
| 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".