429 Short track vs hockey helmets: using finite element analysis to compare strain to the brain
Bibliographic record
Abstract
<h3>Background</h3> Finite element analysis (FEA) is a computational modeling method widely used in materials and mechanical engineering to simulate the strain in a given physical system. The SIMon (Simulation Injury Monitor) is a finite element head model developed by the National Highway Traffic Safety Association in order to study how various impact conditions affect the human brain. <h3>Objective</h3> Compare brain strain in high and low velocity impacts, between short track (ST) and ice hockey (IH) helmets. <h3>Design</h3> Two-group experimental design. <h3>Setting</h3> Data from previous impacts used in SIMon to model the human brain response to impacts. <h3>Patients (or Participants)</h3> 5 different helmet models; 3 ST models and 2 IH models. <h3>Interventions (or Assessment of Risk Factors)</h3> Assessment of ST and IH helmet impact attenuation under various conditions. <h3>Main Outcome Measurements</h3> Cumulative Strain Damage Measure (CSDM) 15, 20 and 25. CSDM is the percentage of brain volume that crosses the 15%, 20% and 25% threshold. This has been shown to correlate with deformation-related brain injuries, such as Diffuse Axonal Injury. <h3>Results</h3> One-way between-helmet ANOVAs for CSDM 15, 20 and 25 in low and high velocity impacts revealed statistical differences in CSDM 15, 20 and 25 [CSDM 15, F(4, 34) = 70.7, p<0.05; CSDM 20, F(4, 34) = 63.4, p<0.05; CSDM 25, F(4, 34) = 32.5, p<0.05]. The trend was that ST helmets outperformed IH helmets in rear, rear-boss and front-boss impacts, but that IH helmets outperformed ST in side impacts. <h3>Conclusions</h3> The results of the FEA reveal a difference between the ST and IH helmets, with ST helmets generally outperforming IH helmets. Interestingly, these results are different than the results optained when comparing linear and rotational acceleration results for these same impacts. Currently, certifications only require peak linear acceleration values be below a certain threshold. However, these studies demonstrate the importance of using various outcome measures to determine the efficacy of helmets in sport.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".