Most Common Neuroinflammatory Origins of Catatonia
Bibliographic record
Abstract
In recent years, research has highlighted the significant role of the immune system in the pathophysiology of catatonia. Emerging evidence indicates that autoimmune encephalitis, such as anti-N-methyl-D-aspartate (anti-NMDA), anti-gamma-aminobutyric acid-A receptor (anti-GABA-AR), and anti-dopamine-2 receptor (anti-D2 R) encephalitis, plays a crucial role in the development of different movement disorders in general and catatonia in particular. In this chapter, we will present three of the most prominent forms of autoimmune encephalitis associated with catatonia depending on their frequency – anti-NMDA, anti-GABA-AR, and anti-D2 R encephalitis. These three neurotransmitter systems, glutamate, GABA, and dopamine, are essential for understanding the pathophysiology of catatonia. Given the rapidly evolving nature of research on autoimmune encephalitis, we aim to inform and sensitize clinicians about the potential association between catatonia (and its signs) and autoimmune encephalitides targeting pathophysiologically relevant neurotransmitters. Our goal is to equip clinicians with the latest findings to improve the recognition and treatment of catatonia in the context of these autoimmune conditions. Finally, we will present the most common red flags that can help identify encephalitis and catatonia early, while emphasizing the need for continued research to better understand the molecular mechanisms and improve treatment options for this complex condition.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".