Clinically Relevant Anti-Neuronal Cell Surface Antibodies in Schizophrenia Spectrum Disorders
Bibliographic record
Abstract
Schizophrenia is a phenotypically heterogeneous and poorly understood disorder. While its etiology is likely multifactorial, immune system dysfunction has increasingly been implicated in its development. As hallucinations and delusions occur frequently and prominently in autoimmune encephalitis (AE), numerous studies have sought to determine whether a small subset of individuals diagnosed with schizophrenia possess anti-neuronal antibodies implicated in AE. Exploring this possibility is of clinical relevance, as identifying individuals with AE who have been misdiagnosed as having a primary psychotic disorder may allow for the implementation of appropriate immune-related therapies as early as possible in the course of the illness, in order to optimize outcomes, reduce illness chronicity, and minimize adverse events. This qualitative review serves to provide an overview of the existing literature on this topic, as well as to update previously published reviews. Although there is some evidence to suggest that in rare cases AE may be misdiagnosed as a primary psychotic disorder, particularly early in the course of the illness, numerous methodological differences between studies likely account for the highly variable findings, and interpretation of the results is particularly limited by a paucity of cerebrospinal fluid data. Moreover, the prevalence of misdiagnosis in chronic and treatment-resistant populations remains understudied. This is particularly problematic, as treatment resistance may represent an enriched population with respect to the presence of anti-neuronal antibodies, and given that such patients have few evidence-based treatment options available to them beyond clozapine.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".