Antiepileptic Drugs as Potential Dementia Prophylactics Following Traumatic Brain Injury
Bibliographic record
Abstract
Seizures and other forms of neurovolatility are emerging as druggable prodromal mechanisms that link traumatic brain injury (TBI) to the progression of later dementias. TBI neurotrauma has both acute and long-term impacts on health, and TBI is a leading risk factor for dementias, including chronic traumatic encephalopathy and Alzheimer's disease. Treatment of TBI already considers acute management of posttraumatic seizures and epilepsy, and impressive efforts have optimized regimens of antiepileptic drugs (AEDs) toward that goal. Here we consider that expanding these management strategies could determine which AED regimens best prevent dementia progression in TBI patients. Challenges with this prophylactic strategy include the potential consequences of prolonged AED treatment and that a large subset of patients are refractory to available AEDs. Addressing these challenges is warranted because the management of seizure activity following TBI offers a rare opportunity to prevent the onset or progression of devastating dementias.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".