Predictions of AIS3+ Thoracic Risks for Belted Occupants in Full-Engagement, Real-World Frontal Impacts: Sensitivity to Various Theoretical Risk Curves
Bibliographic record
Abstract
A new, AIS3+ thoracic risk equation based on chest deflection was derived and assessed for drivers subjected to concentrated (belt-like) loading. The new risk equation was derived from analysis of an existing database of post mortem human subjects in controlled, laboratory sled tests. Binary logistic regression analysis was performed on a subset of the data, namely, 25th-75th percentile men (by weight) from 36-65 years old whose thoracic deformation patterns were due to concentrated (belt-like) loading. Other subsets of data had insufficient size to conduct the analysis. The resulting thoracic risk equation was adjusted to predict the AIS3+ thoracic risks for average-aged occupants in frontal crashes (i.e., 30 years old). Biomechanical scaling was used to derive the corresponding relationships for the small female and large male dummies. The new thoracic risk equations and three other sets of existing equations were evaluated as predictors of real-world crash outcomes. Specifically, thoracic risks associated with belt-only drivers in 1985-1997 model year passenger cars were derived via the four different sets of equations. Comparisons were made from two standpoints: (1) point estimates for 48 km/h potentially barrier-like frontal crashes and (2) aggregate risk estimates derived from simulations of full-engagement, non-rollover, tow-away frontal crashes through 56 km/h. In both cases, the new risk equation agreed with field results. Moreover, an existing thoracic risk equation with a commonly held assumption was shown to significantly overstate observed thoracic field risks.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".