Epidemiology and Impact of Thoracic Trauma on the Mortality of Multi-trauma Patients: Results From a French Road Trauma Registry 1997-2016
Bibliographic record
Abstract
Abstract Thoracic trauma is the third most common cause of death in multi-trauma patients. One of the most frequent mechanism is road traffic accident (RTA). The objective of the present study was to investigate the influence of severe (abbreviated injury scale, AIS≥3) injuries in each body region on the mortality of multi-trauma patients with a particular attention to thoracic trauma. We also described the epidemiology and injury pattern of these patients when presenting with at least one AIS ≥2 thoracic injury (AISThorax≥2). Patients included in the Rhône RTA registry between 1997 and 2016, with at least one AIS ≥2 injury in any body region were included. Two subgroups were defined according to whether patients presented at least one AISThorax≥2 injury or not. Multivariate regression analysis with mortality as outcome was performed. A total of 46,526 patients had at least one AIS≥2 injury, among them 6,382 (13.7%) had at least one AISThorax≥2 injury. Severe thoracic injuries (OR=12.2, 95%CI [8.4;17.7]) were strongly associated with death, second to severe head injuries were (OR=26.8, 95%CI [20.4;35.2]). Chest wall injuries were the most frequent thoracic injury (62.1%, n=5,419) and 52.4% of these were multiple rib fractures. Severe thoracic injury is a priority in multi-trauma patients; both in the detection but also in the management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".