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Record W4411242477 · doi:10.18192/osurj.v4i1.7311

Neurobiological Delusions: Regional Cerebral Blood Flow insights into Cotard’s Syndrome and Schizophrenia

2025· article· en· W4411242477 on OpenAlexaffvenue
Nur Zeynep Camci

Bibliographic record

VenueUniversity of Ottawa Science Undergraduate Research Journal · 2025
Typearticle
Languageen
FieldNeuroscience
TopicSpatial Neglect and Hemispheric Dysfunction
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCerebral blood flowSchizophrenia (object-oriented programming)NeurosciencePsychologyMedicinePsychiatryCardiology

Abstract

fetched live from OpenAlex

Cotard’s syndrome is a clinically rare condition that is characterized by nihilistic or immortality delusions, which are often accompanied by thoughts of suicide, depression, and anxiety. These symptoms vary among individuals but are generally distinguished as a neuropsychiatric disorder. The cause of this condition is further investigated in a recent case study, where a 52-year-old male patient diagnosed with schizophrenia and mild intellectual disability is examined. The patient suffered from several lumbar fractures due to an attempted suicide, and after admission to the hospital, the patient was diagnosed with Cotard’s syndrome. This study aimed to assess the longitudinal changes of regional cerebral blood flow (rCBF) on a single-photon emission computed tomography (SPECT) scan, with respect to a schizophrenic patient displaying Cotard’s syndrome. The symptoms of Cotard’s syndrome appear to dissipate through administration of Lurasidone, which is observed through SPECT and changes in rCBF. The observed correlation between the development of Cotard’s syndrome and an increased rCBF provides a strong basis on the nature of the condition, and enhanced understanding of neuropsychiatric conditions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.032
GPT teacher head0.285
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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Same venueUniversity of Ottawa Science Undergraduate Research JournalSame topicSpatial Neglect and Hemispheric DysfunctionFrench-language works237,207