Diffusion <scp>MRI</scp> abnormalities in adolescent rats given repeated mild traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: Mild traumatic brain injury (mTBI) is a serious health concern in the adolescent population. Repeated mTBI may result in more pronounced deficits, and has been associated with long-term neurological consequences including neurodegeneration. As such, there is a critical need for the development of objective mTBI biomarkers to help guide medical management. Diffusion-weighted imaging (DWI) is an advanced magnetic resonance imaging (MRI) technique that may detect brain abnormalities after mTBI. Diffusion tensor imaging (DTI) is the most commonly applied DWI method, and initial studies have reported DTI changes in mTBI patients. Furthermore, new DWI methods (e.g., track-weighted imaging; TWI) are being developed that may also be sensitive to mTBIs, but remain to be comprehensively studied. METHODS: This study utilized the Awake Closed Head Injury (ACHI) model of mTBI to investigate changes in DTI and TWI following repeated mTBI in adolescent male and female rats. A total of four ACHI impacts, two/day over two consecutive days, were delivered beginning on postnatal day 25. At 1 day and 7 days postinjury, rats were euthanized and brains were collected for DWI analyses. RESULTS: Rats given repeated mTBI displayed changes in fractional anisotropy and radial diffusivity (i.e., DTI measures), as well as track density (i.e., TWI). INTERPRETATION: These findings are consistent with initial DTI findings in mTBI patients, suggest that TWI may complement DTI, support the utility of DWI measures as biomarkers in mTBI, and further validate the ACHI rat model of mTBI.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".