Predicting Cumulative and Maximum Brain Strain Measures From HybridIII Head Kinematics: A Combined Laboratory Study and Post-Hoc Regression Analysis
Bibliographic record
Abstract
Due to growing concern on brain injury in sport, and the role that helmets could play in preventing brain injury caused by impact, biomechanics researchers and helmet certification organizations are discussing how helmet assessment methods might change to assess helmets based on impact parameters relevant to brain injury. To understand the relationship between kinematic measures and brain strain, we completed hundreds of impacts using a 50th percentile Hybrid III head-neck wearing an ice hockey helmet and input three-dimensional impact kinematics to a finite element brain model called the Simulated Injury Monitor (SIMon) (n = 267). Impacts to the helmet front, back and side included impact speeds from 1.2 to 5.8 ms−1. Linear regression models, compared through multiple regression techniques, calculating adjusted R 2 and the F-statistic, determined the most efficient set of kinematics capable of predicting SIMon-computed brain strain, including the cumulative strain damage measure (specifically CSDM-15) and maximum principal strain (MPS). Resultant change in angular velocity, Δω R, better predicted CSDM-15 and MPS than the current helmet certification metric, peak g, and was the most efficient model for predicting strain, regardless of impact location. In nearly all cases, the best two-variable model included peak resultant angular acceleration, α R, and Δω R.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".