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 (AIS Thorax ≥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 AIS Thorax ≥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 AIS Thorax ≥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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".