The Lack of Sex, Age, and Anthropometric Diversity in Neck Biomechanical Data
Bibliographic record
Abstract
Female, elderly, and obese individuals are at greater risk than male, young, and non-obese individuals for neck injury in otherwise equivalent automotive collisions. The development of effective safety technologies to protect all occupants requires high quality data from a range of biomechanical test subjects representative of the population at risk. Here we sought to quantify the demographic characteristics of the volunteers and post-mortem human subjects (PMHSs) used to create the available biomechanical data for the human neck during automotive impacts. A systematic literature and database search was conducted to identify kinematic data that could be used to characterize the neck response to inertial loading or direct head/body impacts. We compiled the sex, age, height, weight, and body mass index (BMI) for 999 volunteers and 110 PMHSs exposed to 5,431 impacts extracted from 63 published studies and three databases, and then compared the distributions of these parameters to reference data drawn from the neck-injured, fatally-injured, and general populations. We found that the neck biomechanical data were biased toward males, the volunteer data were younger, and the PMHS data were older than the reference populations. Other smaller biases were also noted, particularly within female distributions, in the height, weight, and BMI distributions relative to the neck-injured populations. It is vital to increase the diversity of volunteer and cadaveric test subjects in future studies in order to fill the gaps in the current neck biomechanical data. This increased diversity will provide critical data to address existing inequities in automotive and other safety technologies.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".