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Record W2218291150 · doi:10.1016/j.scog.2015.09.001

Neurocognitive normality in schizophrenia revisited

2015· article· en· W2218291150 on OpenAlexaff
R. Walter Heinrichs, Farena Pinnock, Eva Muharib, Leah I. Hartman, Joel O. Goldberg, Stephanie McDermid Vaz

Bibliographic record

VenueSchizophrenia Research Cognition · 2015
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsSt. Joseph’s Healthcare HamiltonYork University
Fundersnot available
KeywordsNeurocognitiveNormalitySchizophrenia (object-oriented programming)PsychologyCognitionAudiologyPsychosisEffects of sleep deprivation on cognitive performanceClinical psychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

score between 40 and 60. Patients (n = 17) and controls (n = 24) meeting the criterion were compared on MCCB domain scores and on independent measures of reading ability, probabilistic and social reasoning. Patients (n = 19) and controls (n = 20) scoring below 40 on the MCCB composite were compared on the same set of measures. Cognitively normal range patients and controls did not differ on estimated premorbid ability or decline and differed only on the Processing Speed domain of the MCCB. Performance did not differ across other domains or on social and probabilistic reasoning tasks. Cognitively below normal range patients and controls showed marked discrepancies between premorbid and current ability, but there were no group differences. In addition, below normal range groups did not differ on any MCCB domain score or in terms of external cognitive measures. Cognitively normal range schizophrenia patients may be largely indistinguishable from normal range controls, with the exception of processing speed performance. More typical schizophrenia patients below the normal range may be indistinguishable from low-performing controls even in terms of processing speed.

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)0.004
metaresearch head score (Gemma)0.005
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: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.003

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 head0.124
GPT teacher head0.394
Teacher spread0.270 · 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 designRandomized trial
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

Citations29
Published2015
Admission routes1
Has abstractyes

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