Modelling and Analysis of the Effect of Angular Velocity and Acceleration on Brain Strain Field in Traumatic Brain Injury
Bibliographic record
Abstract
Traumatic brain injury (TBI) has long been known as one of the most anonymous reasons for death around the world. A presentation of a model of what happens in the process has been under study for many years; and yet it remains a question due to physiological, geometrical and computational complications. Although the facilities for soft tissue modeling have improved and the precise CT-imaging of human head has revealed novel details of brain, skull and the interface (the meninges), a comprehensive FEM model of TBI is still being studied. This study aims to present an optimized model of human head including the brain, skull, and the meninges after a comprehensive study of the previous models. The model is then used to investigate the effects of various sudden velocity-acceleration impulses on the strain field of the brain by using FE method. Next, the results are summed up and compared with an existing criterion on damage threshold in the brain during trauma. To reach this aim, a full geometrical model of a 30-year-old patient’s head has been generated from CT-scan and MR data. The model has been exposed to 20 angular velocity-acceleration pulses. Subsequently, the changes in the strain field have been compared with the results obtained in the previous studies yielding acceptable accordance with a major previous criterion. The results also show that certain criteria can be generated on the threshold of damage in the brain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".