Assessing Changes in Synaptic Plasticity Using an Awake Closed-Head Injury Model of Mild Traumatic Brain Injury
Bibliographic record
Abstract
Mild traumatic brain injuries (mTBIs) are a prevalent health issue in North America. There is increasing pressure to utilize ecologically valid models of closed-head mTBI in the preclinical setting to increase translatability to the clinical population. The awake closed-headed injury (ACHI) model uses a modified controlled cortical impactor to deliver closed-headed injury, inducing clinically relevant behavioral deficits without the need for a craniotomy or the use of an anesthetic. This technique does not normally induce fatalities, skull fractures, or brain bleeds, and is more consistent with being a mild injury. Indeed, the mild nature of the ACHI procedure makes it ideal for studies investigating repetitive mTBI (r-mTBI). Growing evidence indicates that r-mTBI can result in a cumulative injury that produces behavioral symptoms, neuropathological changes, and neurodegeneration. r-mTBI is common in youths playing sports, and these injuries occur during a period of robust synaptic reorganization and myelination, making the younger population particularly vulnerable to the long-term influences of r-mTBI. Further, r-mTBI occurs in cases of intimate partner violence, a condition for which there are few objective screening measures. In these experiments, synaptic function was assessed in the hippocampus in juvenile rats that had experienced r-mTBI using the ACHI model. Following the injuries, a tissue slicer was utilized to make hippocampal slices to evaluate bidirectional synaptic plasticity in the hippocampus at either 1 or 7 days following the r-mTBI. Overall, the ACHI model provides researchers with an ecologically valid model to study changes in synaptic plasticity following mTBI and r-mTBI.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".