Forensic evaluation of pedestrian injuries by FORTIS system and its significance for technical analysis of traffic accidents performed using simulation programs
Bibliographic record
Abstract
Abstract Background Currently, simulation programs are used for a technical analysis of accidents including pedestrians as they provide a great amount of data on the physical parameters of the pedestrian’s body movement and its contacts with the vehicle or the road. In order to be able to make use of the presented options, it is necessary to obtain additional information about detected injuries from forensic doctors in a way utilizable for technical experts. Methods This study includes the results of 250 traffic accidents and approximately 200 real accidents, as well as 255 simulations. The evaluations were based on the investigation of circumstances, accompanying documentation, autopsy findings, photo documentation, and the results of additional expert examinations. We further proceeded in line with the complete autopsy findings in accordance with the requirements of the international classification of diseases. Results Previous practice has shown that the modified forensic system FORTIS, due to its capability to parametrize through localization using the PC Fortis © program, is an important tool to supplement verbal descriptions and localizations of injuries that have been used so far. Conclusions The FORTIS system is a usable and universal means of supplementing verbal medical descriptions for the needs of traffic accident analysts with a scoring system of a high informative value. This, in combination with a video simulation of contacts with a pedestrian’s body during a collision and with values of physical parameters from simulation programs, makes it possible to significantly increase the value of evidence for the needs of the police and courts.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".