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Correlation among personal, social performance and cognitive impairment in male schizophrenic patient

2018· article· en· W2790931330 on OpenAlexaboutno aff
Ramlan Damanik, Elmeida Effendy, Vita Camellia

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2018
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)Montreal Cognitive AssessmentPsychologyCognitionMental illnessPsychiatryCognitive impairmentIndonesianClinical psychologyEveryday lifeQuality of life (healthcare)Intervention (counseling)Mental healthPsychotherapist

Abstract

fetched live from OpenAlex

Schizophrenia is a dramatic mental illness with tragic manifestation. The consequences of the illness are for the individual, affected his or her family and society. Schizophrenia is one of the twenty illness that causes Years Lost due to Disability. Treating only the symptom is insufficient. The aim of treatment must include the quality of life of aschizophrenic person. This study aims to examine the relationship between cognitive impairment and performance of the person with schizophrenia. Cognitive test is scaled with Indonesian version of Montreal Cognitive Assessment (MoCA-Ina), while personal and social performance isscaled with Personal and Social Performance scale. There are many studies that search the relationship between cognitive impairment and social functioning of schizophrenic patients, but this is the first study that uses PSP and MoCA-Ina. Both PSP and MoCA-Ina are easy to use but still have high sensitivity and specificity, and perhaps can build people's interest to use it in clinical practice. Twenty-five male schizophrenic patients were assessed in Prof. M. Ildrem Mental Hospital of North Sumatera Province of Indonesia. Positive correlations between MoCA-Ina and PSP score were identified. Clinicians should pay attention to cognitive and might give some early intervention to it.

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 score0.185
Threshold uncertainty score0.950

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 studies0.0000.003
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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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
Published2018
Admission routes1
Has abstractyes

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