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Record W2079280153 · doi:10.1080/13546800903272059

Insights from the examination of verbal and spatial memory errors in relation to clinical symptoms of patients with recent-onset schizophrenia

2009· article· en· W2079280153 on OpenAlexaff
Caroline Cellard, Sébastien Tremblay, Andrée-Anne Lefèbvre, Louis Laplante, Amélie M. Achim, Roch-Hugo Bouchard, Marc‐André Roy

Bibliographic record

VenueCognitive Neuropsychiatry · 2009
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsRecallPsychologySchizophrenia (object-oriented programming)Verbal memoryCognitionFree recallCognitive psychologyDevelopmental psychologyAudiologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Memory deficits in patients with schizophrenia (SZ) are considered as a key feature of the clinical manifestations of the disease. In order to further examine the role and nature of memory deficits in SZ, the pattern of errors in verbal and spatial serial recall tasks committed by SZ patients was compared to that of healthy controls. We also tested the relationship between these memory errors and clinical symptoms. METHODS: Twenty-seven outpatients with recent-onset SZ and 27 age and gender matched healthy controls had to remember sequences of items (digits or localisations) in a serial recall task. Clinical symptoms were assessed with the PANSS and the SAPS. RESULTS: The results indicate that the number of omissions, intrusions, and transpositions can differentiate patients with SZ from healthy controls. Intrusions and transpositions committed in the verbal domain were associated with the negative subscale of the PANSS. Transposition errors were associated with delusions whether the to-be-remembered information was verbal or spatial. CONCLUSION: The examination of the pattern of errors, in particular that of transpositions, is a more informative cognitive index than the mere analysis of overall performance, and provides a promising target for treatment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.290
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2009
Admission routes1
Has abstractyes

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