Traumatic Brain Injuries
Bibliographic record
Abstract
BACKGROUND: Head impact direction has been identified as an influential risk factor in the risk of traumatic brain injury (TBI) from animal and anatomic research; however, to date, there has been little investigation into this relationship in human subjects. If a susceptibility to certain types of TBI based on impact direction was found to exist in humans, it would aid in clinical diagnoses as well as prevention methods for these types of injuries. OBJECTIVE: To examine the influence of impact direction on the presence of TBI lesions, specifically, subdural hematomas, subarachnoid hemorrhage, and parenchymal contusions. METHODS: Twenty reconstructions of falls that resulted in a TBI were conducted in a laboratory based on eyewitness, interview, and medical reports. The reconstructions involved impacts to a Hybrid III anthropometric dummy and finite element modeling of the human head to evaluate the brain stresses and strains for each TBI event. RESULTS: The results showed that it is likely that increased risk of incurring a subdural hematoma exists from impacts to the frontal or occipital regions, and parenchymal contusions from impacts to the side of the head. There was no definitive link between impact direction and subarachnoid hemorrhage. In addition, the results indicate that there is a continuum of stresses and strain magnitudes between lesion types when impact location is isolated, with subdural hematoma occurring at lower magnitudes for frontal and occipital region impacts, and contusions lower for impacts to the side. CONCLUSION: This hospital data set suggests that there is an effect that impact direction has on TBI depending on the anatomy involved for each particular lesion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".