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Record W1999941264 · doi:10.1521/pedi.2011.25.2.260

Endophenotypes and the Diagnosis of Personality Disorders

2011· article· en· W1999941264 on OpenAlexaff
Joel Paris

Bibliographic record

VenueJournal of Personality Disorders · 2011
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsJewish General HospitalMcGill University
Fundersnot available
KeywordsEndophenotypePsychologyConstruct (python library)Categorical variablePersonality disordersPersonalityEtiologyCognitive psychologyBig Five personality traitsPsychopathologyClinical psychologyPsychiatryDevelopmental psychologyCognitionSocial psychology

Abstract

fetched live from OpenAlex

It has been suggested that psychiatric diagnosis should come to depend on endophenotypes, in order to define more precisely the mechanisms behind mental disorders. This construct is associated with the assumption that mental processes can be reduced to activity at a neuronal level. The approach has had a strong influence on the conceptual basis of proposals for DSM-5, but could be consistent either with categorical or dimensional diagnosis. However, application of endophenotypes to personality disorders is unlikely for the foreseeable future, given an insufficient knowledge of etiology and pathogenesis.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.036
GPT teacher head0.301
Teacher spread0.265 · 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 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

Citations21
Published2011
Admission routes1
Has abstractyes

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