P.121 Memantine inhibits cortical spreading depolarization and improves neurovascular function following traumatic brain injury: results of a randomized pre-clinical trial
Bibliographic record
Abstract
Background: Cortical spreading depolarization (CSD) is associated with poor outcomes following traumatic brain injury (TBI). Here we aimed to: (1) determine the effect of NMDA-receptor antagonism on CSDs in healthy and TBI animals in vivo; and (2) conduct a randomized pre-clinical trial (RCT) of memantine for prevention neurological decline following repetitive mild TBI (rmTBI). Methods: Rodents received either one moderate (n = 23) or four daily mild (rmTBI; n = 30) head impacts (weight drop). Sham animals received brief anesthetic without TBI (n = 40). 93 animals underwent cranial window surgery with electrocorticographic (ECoG) monitoring and electrically triggered CSDs. Ketamine (100uM topical or 25 mg/kg IP) and memantine (10 mg/kg IP) were tested in vivo. An RCT was conducted (N=31) using memantine (10 mg/kg) or saline (2.5 cc/kg) for rmTBI with daily neurobehavioural testing. Results: In TBI animals, ketamine or memantine inhibited CSDs in 44-88%, and 50-67% of cases, respectively. Near-DC/AC-ECoG amplitude was reduced by 44-75% and 52-67%, and duration by 39-87% and 61-78%, respectively. RCT animals that received memantine had higher mean neurological scores (9.27 (SD 3.08) vs. 5.56 (SD 3.05), p< 0.001) vs. saline. Conclusions: Memantine suppressed CSDs following TBI, in vivo. In a pre-clinical RCT of rmTBI, memantine prevented neurological decline.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".