Beneficial cognitive effect of lamotrigine in severe acquired brain injury: A case report
Bibliographic record
Abstract
Background Acquired brain injuries (ABI) can cause various negative sequelae, including cognitive impairment, leading to poor functional outcomes for patients. Evidence is limited on pharmacological interventions to effectively improve the cognitive status of these patients. This study aims to provide evidence for the use of lamotrigine to improve the cognitive status of patients with severe ABI. Case presentation We report the case of a 29-year-old man who suffered a severe traumatic brain injury secondary to a motor vehicle collision. When admitted to our rehabilitation program three months later, he was in a minimally conscious state, achieving Level III on the Rancho Los Amigos Cognitive Scale. Post-traumatic seizures were well controlled with levetiracetam. Multiple neurostimulants including methylphenidate, amantadine, and venlafaxine were trialed with minimal benefit, thereby prompting the switch of his antiepileptic medication to lamotrigine five months after his injury. The introduction of lamotrigine was followed by relatively rapid and significant improvement in arousal, cognition and communication that preceded levetiracetam being tapered. The patient continued making functional gains over the following year while using lamotrigine. Conclusions Lamotrigine may potentially provide cognition-enhancing effects independent of its known anti-epileptic properties in patients with severe ABI. Further research is required on the role of lamotrigine in patients with ABI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".