Development and tolerability of a novel virtual- and proprioception-based car crash simulator as a new research tool in motor vehicle trauma research
Bibliographic record
Abstract
Purpose: Investigations of causal theories of neck pain (NP) following motor vehicle crashes (MVC) has been difficult, as simulation is limited. Thus, we sought to evaluate tolerability to a novel virtual reality (VR)-based road collision simulator and screen for adverse reactions. Materials and Methods: Cross-sectional study. 25 healthy participants were exposed to a novel VR-based rear-end MVC with a small perturbation (0.2 g). The Simulator Sickness Questionnaire (SSQ) and Presence Questionnaire (PQ) were measured post-exposure and adverse reactions were recorded. Results: The system was well tolerated with no adverse reactions, however one participant reported NP the following day not lasting longer than 48 h. Participants reported low levels of simulator sickness (mean SSQ = 23.49 ± 21.98, range = 0.00 to 89.76; max score = 235.62), while presence (mean PQ = 91.04 ± 14.08, range = 54.00 to 112.00; max score = 133), was lower than literature recommendations. Conclusion: A VR-based road collision simulator can be safely used to explore the phenomenon of a motor vehicle crashes under controlled circumstances. Future work is needed to optimize the virtual reality environment and to investigate the effects of crash parameters.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".