Field-based Assessments of Various AIS2+ Head Risk Curves for Frontal Impact
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">In the present study, various risk curves for moderate-to-fatal head injury (AIS2+) were theoretically assessed by comparing model-based injury rates with field-based injury rates. This was accomplished by applying the risk curves in corresponding field models. The resulting injury rates were considered from two perspectives: aggregate (0-56 kph events) and point-estimate (higher-speed, barrier-like events).</div><div class="htmlview paragraph">Four risk curves were studied: a HIC15-based curve from <span class="xref">Mertz et al. (1997)</span>, a BRIC-based curve from <span class="xref">Takhounts et al. (2011)</span>, a BrIC-based curve from <span class="xref">Takhounts et al. (2013)</span> and a Concussion-Correlate-based curve from <span class="xref">Rowson et al. (2013)</span>.</div><div class="htmlview paragraph">The field modeling pertained to adult drivers in 11-1 o'clock, towaway, full-engagement frontal crashes in the National Automotive Sampling System (NASS, calendar years = 1993-2012), and the model-year range of the passenger vehicles was 1985-2010. The attendant inhomogeneity of the restraint systems was approximated by four representative systems.</div><div class="htmlview paragraph">Prior to the field modeling, base models were developed and validated to acceptable levels. Specifically, there were seven assessments of head-impact tests (avg PctDiff = 5.2%) and nine assessments of representative restraint-system models relative to fleet-wide tests (avg PctDiff = 5.0% relative to fleet medians).</div><div class="htmlview paragraph">Subsequently, 36 comparisons were conducted via the field modeling relative to NASS (i.e., four curves and nine aggregate/point assessments). On average, the Concussion-Correlate-based curve demonstrated the best fidelity from the aggregate perspective (+7% difference), with the following rank ordering: Concussion Correlate, HIC15, BRIC, and BrIC. As for the point-estimate perspective, the HIC15 curve demonstrated the best fidelity (−26% difference), rank ordered as HIC15, Concussion Correlate, BRIC, and BrIC. The data underlying these results indicated that more study is needed to improve the fidelity of curve-based risk estimation.</div></div>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".