Road traffic accident-related thoracic trauma: Epidemiology, injury pattern, outcome, and impact on mortality—A multicenter observational study
Bibliographic record
Abstract
BACKGROUND: Thoracic trauma is a major cause of death in trauma patients and road traffic accident (RTA)-related thoracic injuries have different characteristics than those with non-RTA related thoracic traumas, but this have been poorly described. The main objective was to investigate the epidemiology, injury pattern and outcome of patients suffering a significant RTA-related thoracic injury. Secondary objective was to investigate the influence of serious thoracic injuries on mortality, compared to other serious injuries. METHODS: We performed a multicenter observational study including patients of the Rhône RTA registry between 1997 and 2016 sustaining a moderate to lethal (Abbreviated Injury Scale, AIS≥2) injury in any body region. A subgroup (AISThorax≥2 group) included those with one or more AIS≥2 thoracic injury. Descriptive statistics were performed for the main outcome and a multivariate logistic regression was computed for our secondary outcome. RESULTS: A total of 176,346 patients were included in the registry and 6,382 (3.6%) sustained a thoracic injury. Among those, median age [IQR] was 41 [25-58] years, and 68.9% were male. The highest incidence of thoracic injuries in female patients was in the 70-79 years age group, while this was observed in the 20-29 years age group among males. Most patients were car occupants (52.3%). Chest wall injuries were the most frequent thoracic injuries (62.1%), 52.4% of which were multiple rib fractures. Trauma brain injuries (TBI) were the most frequent concomitant injuries (29.1%). The frequency of MAISThorax = 2 injuries increased with age while that of MAISThorax = 3 injuries decreased. A total of 16.2% patients died. Serious (AIS≥3) thoracic injuries (OR = 12.4, 95%CI [8.6;18.0]) were strongly associated with mortality but less than were TBI (OR = 27.9, 95%CI [21.3;36.7]). CONCLUSION: Moderate to lethal RTA-related thoracic injuries were rare. Multiple ribs fractures, pulmonary contusions, and sternal fractures were the most frequent anatomical injuries. The incidence, injury pattern and mechanisms greatly vary across age groups.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".