P1‐057: Lithium Carbonate and Verbal Memory Improvement in Post‐Stroke Patients: Preliminary Report
Bibliographic record
Abstract
Ischemic stroke impacts cognitive functions such as memory and increases risk of dementia. Lithium is a mood stabilizer that has been shown to increase grey matter volume in bipolar patients. In other populations, increased brain volume after drug treatment has been associated with improved verbal memory. Therefore, we hypothesized that treatment with the neurotrophic agent lithium would be associated with improvement in verbal memory in post-stroke patients. Patients who met the World Health Organization MONICA Project and National Institute of Neurological Disorders and Stroke (WHO-NINDS) criteria of for a recent (<1 year) ischemic cortical stroke (evidenced by MRI report from stroke neurologist) were recruited. Lithium carbonate was administered open-label for 60 days; plasma concentrations of lithium were maintained at a range of 0.4-0.8mmol/L. We assessed verbal memory using delayed recall in Hopkins Verbal Learning Test-Revised (HVLT-R) at the start and end of treatment. Repeated-measures ANOVA was used to compare delayed recall z-scores over time, with cumulative lithium dose as a covariate. We also assessed stroke severity (National Institutes of Health Stroke Scale (NIHSS)) and cognition (Montreal Cognitive Assessment (MoCA), Standardized Mini-Mental State Evaluation (sMMSE)) at baseline and termination. To date, 11 patients (45% male, mean (SD) age = 70.3 (12.1), sMMSE = 26.8 (3.4), MoCA = 20.9 (5.0), HVLT-R delayed recall z-score = -1.3 (1.0)) have been recruited, on average, 87 (±68) days after mild stroke (NIHSS score ≤2). Cumulative lithium dose received ranged from 0 (screen drop) to 26850 mg. Lithium was discontinued in 3 patients due to tolerability issues but there were no serious adverse events. Cumulative lithium dose was significantly associated with improvement in delayed recall over time (F=5.41, p=0.045). There was no change in the other measures. These initial results suggest that lithium treatment is tolerated by many post-stroke patients, and may be associated with improved verbal memory after stroke, with a preliminary signal suggesting a dose response relationship. These findings are consistent with lithium’s suggested neuroprotective and neurotrophic effects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".