Poster Session II, July 14th 2010 — Abstracts
Bibliographic record
Abstract
Since the introduction of helmets in American football the rate of traumatic brain injuries (TBI) has decreased, however, the incidence of mild traumatic brain injury (mTBI) appears to be unaffected. Currently finite element driven research is beginning to shed light on the difference in mechanism between TBI and mTBI, focusing mainly on the effects of linear and angular acceleration. From this it is hoped that a method to prevent these injuries may be developed. The research presented here will use a helmet evaluation protocol developed at the University of Ottawa to analyze the performance of currently used American football helmets using a finite element model (FEM). A helmeted hybrid III headform, equipped with a 3-2-2-2 accelerometer array was impacted according to the developed protocol. The x, y and z linear and angular acceleration data was then used to power the FEM. The results indicate that both helmets perform similarly when evaluated on linear acceleration alone, but differ angularly. Linear acceleration results were well below proposed limits for brain injury; however the FE model indicated a 50% likelihood of mTBI. There were also situations where linear and angular acceleration was nearly identical, but had different strain results, indicating that peak values may not be the most important curve characteristic. The results support two conclusions: (1) Angular accelerations seem to be more influential in the creation of brain strains in this model, and (2) Designing helmets by linear acceleration alone may not be ideal when brain strains are considered to be a factor in mTBI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.512 | 0.341 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".