SP6.12 - Do Pregnant Females Experiencing Physical Traumatic Injury have Different Mortality Outcomes Compared to Non-pregnant Females? A Systematic Review and Metanalysis
Bibliographic record
Abstract
Abstract Aims To collate and analyse literature capturing mortality rates in pregnant and non-pregnant individuals of childbearing age who experienced physical trauma. Additionally, to identify any statistically significant differences in interventions received. Methods Comprehensive searches of PubMed, MEDLINE, and grey literature were conducted using MeSH terms. Data extraction by two independent reviewers was followed by quality assessments using the Newcastle-Ottawa Scale. A random effects meta-analysis assessed the effect of pregnancy on mortality post-physical trauma, employing risk ratios (RR) and 95% confidence intervals. Publication bias was evaluated through linear regression, Eggers test, and funnel plots. Results Twelve studies met eligibility criteria, yielding 1,113,376 pregnant and 18,185,789 non-pregnant females of childbearing age who experienced physical trauma. Low publication bias was observed. Pregnant females demonstrated a 35% mortality reduction vs. non-pregnant females (pooled RR: 0.65 [0.47-0.89], p=0.008), though significant heterogeneity existed across studies (I2 = 77%). A subgroup analysis focusing on mechanism of injury found the mortality reduction in pregnancy increased to 59% in motor vehicle accidents (pooled RR = 0.41 [0.32-0.52], p<0.001) with minimal study heterogeneity (I2 = 0%). Pregnant (vs. non-pregnant) females were less likely to be intubated (pooled RR = 0.69 [0.58, 0.81], p=<0.001, I2 = 9), but there were no other significant differences in the types of intervention received. Conclusions Pregnant females have a lower mortality risk from physical trauma than non-pregnant counterparts, particularly where the mechanism of injury is a motor vehicle accident. Additional research is warranted to elucidate the underlying factors contributing to this observed difference.
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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 | 0.031 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.069 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".