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Record W2982490229

Does Schizoaffective Disorder explain the mental illnesses of Robert Schumann and Vincent Van Gogh?

2018· article· en· W2982490229 on OpenAlexaff
Yasmeen Cooper, Mark Agius

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsTrinity College
Fundersnot available
KeywordsSchizoaffective disorderBipolar disorderAcute intermittent porphyriaSchizophrenia (object-oriented programming)PsychiatryPsychologyPsychosisMoodDementiaPediatricsClinical psychologyMedicineDiseasePorphyriaInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

The geniuses Robert Schumann and Vincent Van Gogh show striking similarities both in the longitudinal nature of the progression of their illnesses, and the symptoms they experienced. There have been physiological explanations posed for both men, including Meniere's disease, tertiary syphilis, acute intermittent porphyria, terpenoid and lead poisoning, intracranial masses, temporal lobe epilepsy and dementia caused by vascular hypertension. The evidence for these physiological explanations is assessed. Schizophrenia and Bipolar disorder have also both been postulated to explain the symptoms of the two men, but neither man perfectly fits the diagnostic criteria for either. Schizoaffective disorder is a term used to describe patients who experience symptoms from both the psychosis of Schizophrenia and the mood disorders of Bipolar disorder. This paper aims to explain why Schizoaffective disorder explains the symptomology of these men better than either Schizophrenia or Bipolar disorder does alone. Schizoaffective disorder, however, did not exist as a diagnosis when Van Gogh and Schumann were alive, and so was not considered by their physicians.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.232
Teacher spread0.218 · 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 designTheoretical or conceptual
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

Citations6
Published2018
Admission routes1
Has abstractyes

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Same venuePubMed→Same topicGenetic Neurodegenerative Diseases→French-language works237,207→