Poster Session III, July 15th 2010 — Abstracts Finite element analysis of the effect of loading curve shape on brain injury predictors
Bibliographic record
Abstract
Traumatic (TBI) and mild traumatic brain injury (mTBI) occur in everyday incidents as well as sporting events, and their prediction has become important factor in prevention. Currently, the prediction of these injuries is limited to peak linear and angular acceleration values derived from laboratory reconstructions. With the advent of more powerful computers, the use of finite element modelling has become a research tool which is used in an effort to link linear and angular acceleration values to brain injury parameters such as stress and strain. However it remains unclear as to what aspect of these curves contributes to brain tissue damage. This research will use the University College Dublin Brain Trauma Model (UCDBTM) to analyze three distinct curve shapes, independently in each axis of linear and angular acceleration, and their effect on currently used predictors of TBI and mTBI. Three curve inputs were run through the UCDBTM, curve A had a late peak, curve B had an early peak, and curve C had a continuous plateau. All three curves had equivalent areas. Each curve was run in each axis, x, y, and z for linear and angular acceleration outputs. The results indicate that Curve A produced consistently higher maximum principal strains and Von Mises Stress than the other two curve types. Curve C consistently produced the lowest values, with Curve B being lowest in only 2 cases. The areas of peak Von Mises Stress and Principal strain also varied depending on curve shape and acceleration input.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.167 | 0.057 |
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".