A comparison of anti-whiplash seats during low/moderate speed rear-end collisions
Bibliographic record
Abstract
Objectives: The Insurance Institute for Highway Safety (IIHS) rates automotive seats as good, acceptable, marginal, and poor on their abilities to prevent whiplash injuries during rear-end collisions. The goal of this study was to compare the performance of some good- and poor-rated seats at speed changes below 16 km/h where some whiplash injuries occur.Methods: A BioRID II anthropometric test device (ATD) underwent rear-end collisions from 2 to 14 km/h while seated on one of two Volvo Whiplash Prevention seats (WHIPS), a Saab Active Head Restraint seat (SAHR), or a General Motors High Retention seat (GMHR). The WHIPS and SAHR seats were rated good whereas the GMHR seat was rated poor by the IIHS. The ATD’s kinematics, kinetics and three neck injury criteria were evaluated across the range of collision severities.Results: Most of the head and torso kinematics, kinetics and injury criteria exhibited graded responses with increasing collision severities. Only head extension angle remained relatively similar across all speed changes. Differences between the good- and poor-rated seats were most apparent in the upper neck loads and moments, and head retraction for speed changes greater than 6 km/h.Conclusions: The relatively similar occupant responses across all seats could explain the marginal reductions in whiplash injury risk between good- and poor-rated seats in field studies. Further research into the design of anti-whiplash devices is required to better understand the link between occupant response and injury, and to better mitigate the risk of whiplash injuries during rear-end collisions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".