Olanzapine vs. magnesium valproate vs. lamotrigine in anti-N-methyl-D-aspartic acid receptor encephalitis: a retrospective study
Bibliographic record
Abstract
BACKGROUND: This study aimed to compare the impact of olanzapine, magnesium valproate, and lamotrigine as adjunctive treatments for anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis. And it is expected to add supporting points related to the rebalance of neurotransmitters in the brain through adjuvant therapy in the clinical management of anti-NMDAR encephalitis. METHODS: This retrospective study included patients diagnosed with anti-NMDAR encephalitis who received standardized immunotherapy at Hunan Brain Hospital between January 2018 and December 2020. RESULTS: Compared to the olanzapine group, both the magnesium valproate and lamotrigine groups showed lower scores on the positive and negative symptom scale (PANSS) total score after 3 weeks of treatment (all P < 0.05). The Montreal Cognitive Assessment Scale (MoCA) scores in the magnesium valproate and lamotrigine groups were significantly higher than in the olanzapine group after 3 weeks and 3 months of treatment (all P < 0.05). After 3 months of treatment, the proportions of patients with a modified Rankin scale score (mRS) of 0-1 in the magnesium valproate and lamotrigine groups were significantly higher than in the olanzapine group (all P < 0.05). The electroencephalogram (EEG) abnormality ranks at 3 months were significantly lower in the magnesium valproate and lamotrigine groups compared with the olanzapine group (all P < 0.05). Furthermore, the Glx/Cr ratio significantly decreased after 3 months of treatment (all P < 0.05) in the magnesium valproate and lamotrigine groups, while the Glx/Cr ratio in the olanzapine group showed no significant change (P > 0.05). CONCLUSION: Compared with olanzapine, the addition of magnesium valproate or lamotrigine to immunotherapy might be associated with a lower PANSS score, higher MoCA score, and lower mRS score. The improvement of neurological functions and cognitive function may be related to the decreased Glx/Cr ratio.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".