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Record W2133537173 · doi:10.1186/1752-1947-7-134

Monozygotic twins with early-onset schizophrenia and late-onset bipolar disorder: a case report

2013· article· en· W2133537173 on OpenAlexaff
Richard O’Reilly, E. Fuller Torrey, Jay Rao, Shiva M. Singh

Bibliographic record

VenueJournal of Medical Case Reports · 2013
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsSt Joseph's Health CareWestern University
Fundersnot available
KeywordsBipolar disorderSchizophrenia (object-oriented programming)Monozygotic twinPsychiatryMedicineBipolar I disorderMoodTwin studyMood stabilizerPediatricsPsychologyClinical psychologyMania

Abstract

fetched live from OpenAlex

INTRODUCTION: Schizophrenia and bipolar disorder are generally considered to be distinct illnesses. One piece of evidence supporting their distinctness is the rarity of schizophrenia and bipolar disorder occurring in monozygotic co‒twins. CASE PRESENTATION: We describe a well-characterized pair of African American, female, monozygotic twins assessed at 53 years of age.Case 1: Twin A developed psychotic symptoms at age 23. She was hospitalized and diagnosed with schizophrenia. Twin A was subsequently hospitalized several more times and was consistently diagnosed as suffering from schizophrenia. At the time of assessment, Twin A was single, lived with her parents and attended a day program. Case 2: In contrast, Twin B worked in a professional career, married and raised a family. She remained well until age 48 when she developed a depressive disorder requiring medication treatment. Four years later, Twin B abruptly developed grandiose delusions and mood-congruent auditory hallucinations. She was hospitalized and diagnosed with a manic episode. Since then Twin B has remained symptom-free on the mood stabilizer sodium valproate. CONCLUSION: Schizophrenia and bipolar disorder can occur in identical co-twins. We speculate on what it tells us about the meaning of discordance and the putative role of de novo mutations.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.286
Teacher spread0.273 · 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 teacher head, not a consensus.

Study designCase report
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
Published2013
Admission routes1
Has abstractyes

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