A Neuropathological Study of Diffuse Vascular Injury in Fatal Motor Vehicle Collisions
Bibliographic record
Abstract
In Canada, 42 929 people were involved in fatal motor vehicle collisions (MVCs) between 1999 and 2018. Traumatic brain injuries (TBIs), including diffuse vascular injury (DVI), were the most frequent cause of death. The neuroanatomical injury pattern and severity of DVI in relation to data on MVC dynamics and other MVC factors were the focus of the current study. Five cases of fatal MVCs investigated by Western University's Motor Vehicle Safety (MOVES) Research Team with the neuropathological diagnosis of DVI were reviewed. DVI was seen in single and multiple vehicle collisions, with/without rollover and with/without partial occupant ejection. DVI occurred regardless of seatbelt use and airbag deployment and in vehicles equipped with/without antilock brakes. All DVI cases sustained head impacts and had focal TBIs, including basal skull fractures and subarachnoid hemorrhages. DVI was seen in MVCs that ranged in severity based on the change in velocity (delta-V) during the crash (minimum 31 km/hour) and occupant compartment intrusion (minimum 25 cm). In all cases, DVI in frontal white matter, corpus callosum and pontine tegmentum were common. In cases with more extensive DVI, pronounced vehicle rotation occurred before the final impact. Extensive DVI was seen in drivers who experienced sudden acceleration during vehicle rotation and deceleration.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".