Epidemiology of pregnant patients with major trauma in Victoria
Bibliographic record
Abstract
OBJECTIVE: Trauma is one of the most common contributors to maternal and foetal morbidity and mortality. The aim of the present study was to describe the characteristics and outcomes of major trauma in pregnant patients using a population-based registry. METHODS: Registry-based study using data from the Victorian State Trauma Registry (VSTR), a population-based database of all hospitalised major trauma (death due to injury, Injury Severity Score [ISS] ≥12, admission to an intensive care unit [ICU] for more than 24 h and requiring mechanical ventilation for at least part of their ICU stay or urgent surgery) in Victoria, Australia, from 1 July 2007 to 30 June 2019. Pregnant patients with major trauma were identified on the VSTR. We summarised patient data using descriptive statistics. RESULTS: Over the 12-year study period, there were 63 pregnant major trauma patients. Fifty-two (82.5%) patients sustained injuries resulting from road transport collisions. The maternal survival rate was 98.4% and the foetal survival rate was 88.9%. Thoracic injury was the most common injury (25/63), followed by abdominal injury (23/63). Eighty-six percent of the third trimester patients (19/22) were transported directly to a major trauma service with capacity for definitive care of the pregnancy. CONCLUSION: The present study demonstrated road transport injury was the most common mechanism of injury and both maternal survival rates and foetal survival rates were high. This information is essential for trauma care system planning and public health initiatives to improve the clinical management and outcomes of pregnant women with major trauma.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".