Evaluation of Aortic Injury in Driver Fatalities Occurring in Motor Vehicle Accidents in the State of Maryland for 2003 and 2004
Bibliographic record
Abstract
Incorporating epidemiological and pathologic factors, a retrospective analysis of aortic injury and driving fatalities was conducted. To better understand the mechanism of injury, data were compiled for decedent demographics, autopsy and toxicology findings, and accident circumstances, with emphasis on directional impact. Review of the autopsy files of the Office of the Chief Medical Examiner in the State of Maryland in 2003 and 2004, identified 150 cases of aortic injury recorded in 537 autopsied drivers. Aortic lacerations occurred in 96% of the cases with aortic injury, two thirds of which were complete or near complete transections. A large percentage of cases involved a side impact collision. Consistent with extant research on frontal and lateral impacts, the majority of aortic injuries occurred at the ligamentum arteriosum. Also, the mechanism of aortic injury seems to be similar for side and frontal impact collisions, involving a combination of rapid deceleration forces along with chest and/or upper abdominal compression. This study emphasizes the importance of side impact collisions as a cause of aortic injury. Aortic lacerations have a high mortality rate and better motor vehicle design may prevent this type of injury.
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 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 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".