Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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) | 0.000 | 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 it