Schizophrenia vs. encephalitis: A neuropsychological case study
Bibliographic record
Abstract
From presenting with flu-like symptoms, seizures, and erratic behaviour including hallucinations, to being dismissed as "partying too much" and misdiagnosed with schizophrenia before the ultimate provision of a neurological explanation - encephalitis; this was a true sequence of events for the 24 year old female, Susannah Cahalan, who suddenly became ill with a mysterious illness that was misdiagnosed even after extensive evaluation until a neurologist was able to diagnose and effectively treat her (Cahalan, 2012; Barrett, 2016). Susannah's case bemused the medical field and became the plot of a book that subsequently garnered attention, large enough to be adapted into a movie, titled "Brain on Fire" (Barrett, 2016). Her case illustrated the exquisite interplay of neurology, physiology, and neuropsychology, complicated by personality traits and stereotypical behaviours observed in young adulthood, the period in which psychiatric illnesses also often begin to manifest. Unfortunately, while Susannah's case is rare, it is not unique. The following illustrates a case, similar to Susannah's, in which fluorodeoxyglucose-positron emission tomography (FDG-PET) scans, chronological history, and neuropsychological test results supported a diagnosis of encephalitis, while symptom presentation, response to treatment, and neurological consultation, suggested a diagnosis of schizophrenia, demonstrating a significant overlap in presentation of these two disorders and the importance of a multidisciplinary approach to diagnosis and treatment (APA, 2013; Lancaster, 2016). This case illustrates the complexity of the art and science of diagnostics during the developmental period, reminding us as professionals of the importance of thoroughly reviewing a patient's medical history and of the vital contributions each discipline can make when attempting to diagnose and treat complex presentations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".