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Record W2134729998 · doi:10.1139/jpn.0412

Should cognitive deficit be a diagnostic criterion for schizophrenia?

2004· article· en· W2134729998 on OpenAlexaffvenue
Ralph Lewis

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2004
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)PsychosisCognitionPsychologyCognitive deficitPsychiatryDementia praecoxClinical psychologyCognitive impairment

Abstract

fetched live from OpenAlex

This review examines the question of whether cognitive deficits in schizophrenia are sufficiently reliable, stable and specific to warrant inclusion in the diagnostic criteria for schizophrenia. The literature provides evidence that cognitive deficits are highly prevalent and fairly marked in adult patients with schizophrenia. Similar deficits have been found in children and adolescents with schizophrenia, and in children before they exhibit the signs and symptoms of schizophrenia. These deficits may in fact be central to the pathophysiology underlying the development of overt psychosis in schizophrenia. The deficits appear to be relatively stable across the course of the illness. They are generally more severe in schizophrenia than in affective disorders and may have a relatively specific pattern in schizophrenia. It is concluded that the evidence that cognitive deficits are a core feature of schizophrenia is sufficiently compelling to warrant inclusion of these deficits in the diagnostic criteria for schizophrenia, at least as a nonessential criterion.

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)0.004
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.052
GPT teacher head0.349
Teacher spread0.297 · 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
GenreCommentary

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

Citations107
Published2004
Admission routes2
Has abstractyes

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