N-Methyl-D-Aspartate receptor antagonist treatment in traumatic brain injury: a systematic review of the clinical studies
Bibliographic record
Abstract
INTRODUCTION: Traumatic brain injury (TBI) is a leading cause of long-term disability. N-methyl-D-aspartate receptor (NMDAR) signaling constitutes an important target for pharmacological treatment options. METHODS: The authors have systematically reviewed primary clinical literature reporting on FDA-approved NMDAR antagonist treatment in TBI, based on a set of pre-defined eligibility criteria. Risk of bias assessment was performed using Scottish Intercollegiate Guidelines Network (SIGN) recommendations. Patient characteristics, treatment conditions, and outcomes were reported according to PRISMA guidelines. RESULTS: This review of five clinical literature databases identified 32 eligible studies. Of 1,827 included patients, the majority (74.8%) experienced severe TBI (weighted mean baseline GCS 6.35). Amantadine (24 studies) variably influenced functional recovery and was linked to adverse effects. Ketamine (five studies) variably lowered intracranial pressure and suppressed spreading depolarization. Memantine and dextromethorphan (2 and 1 studies, respectively) showed favorable safety profiles, though data were limited. Across controlled studies, there was a 0.46 (95% CI: 0.16-0.76) weighted mean difference between control and intervention, favoring NMDAR antagonist treatment. CONCLUSIONS: : CRD42024539051.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".