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Record W2759074308 · doi:10.5455/nys.116.1480065920

Perseveration is not Related with Functionality in Bipolar I Disorder with a Psychotic Mood Episode

2017· article· en· W2759074308 on OpenAlexaboutno aff
Sevda Gümüş Şanlı, Nurhan Fıstikçı, Ömer Saatçioğlu, Evrim Erten, Gülsüm Cantürk

Bibliographic record

VenueNeuropsychiatric Investigation · 2017
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsBipolar disorderMoodPsychologyPsychiatryBipolar I disorderClinical psychologyMania

Abstract

fetched live from OpenAlex

Aim: The aim of this study was to assess perseverative errors and responses as measured by the Wisconsin Card Sorting Test (WCST) in remitted bipolar I disorder patients having at least one previous psychotic mood episode and investigated the relationship between perseveration, areas of functionality, and clinical features. Methods: In the current study 48 remitted patients with bipolar I disorder diagnosed with DSM-IV criteria, and 45 socio-demographically matched healthy controls were consecutively enrolled. Socio-demographic and clinical characteristics form, Young Mania Rating Scale, Hamilton Depression Rating Scale, Bipolar Disorder Functioning Questionnaire (BDFQ), the Wisconsin Card Sorting Test (WCST), the DSM-IV Structured Clinical Interview for axis I Disorders, Montreal Cognitive Assessment Scale were used. Results: The distribution of age, gender and years of education between the patient and the control group was similar. In the patient group scores of perseverative errors in WCST was found to be higher than controls but there were no significant association between the total BDFQ scores of patients and the number of perseverative errors or responses. Conclusion: Perseverative errors and responses on the WCST was significantly higher in the remitted patients with bipolar I disorder who had at least one psychotic mood episode, when compared with the healthy controls, but this impairment did not have any impact on functioning.

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 score0.013
Threshold uncertainty score0.712

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 head0.015
GPT teacher head0.246
Teacher spread0.230 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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