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Record W4408927918 · doi:10.63050/jpps.22.1.977

FREQUENCY AND ASSOCIATION OF COGNITIVE DYSFUNCTION IN SCHIZOPHRENIA: A CROSS-SECTIONAL STUDY FROM A TERTIARY CARE HOSPITAL IN PAKISTAN

2025· article· en· W4408927918 on OpenAlexaboutno aff
Zainab Sher, Fawad Suleman, Samiya Iqbal, Amber Tahir, S. Rafi

Bibliographic record

VenueJournal of Pakistan Psychiatric Society · 2025
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsCross-sectional studyTertiary careSchizophrenia (object-oriented programming)Association (psychology)CognitionMedicineTertiary levelPsychiatrySchizophrenia spectrumClinical psychologyPsychologyPsychosisFamily medicinePathologyPsychotherapist

Abstract

fetched live from OpenAlex

OBJECTIVETo assess the frequency of cognitive impairment and sociodemographic association among patients with schizophrenia presenting to a tertiary care hospital in Pakistan.STUDY DESIGNDescriptive cross-sectional studyPLACE AND DURATION OF STUDYOutpatient Department of Psychiatry at Dr Ruth K.M. Pfau. Civil Hospital Karachi, Pakistan. The duration of the study was 6 months from February 6, 2020, to August 5, 2020. METHODOne hundred thirty patients with schizophrenia were assessed using the Urdu version of Montreal cognitive assessment questionnaire. RESULTSout of the 100 patients, 66 had significant cognitive impairment accounting for more than half of the study population at 50.8%. Age, duration of illness and gender showed significant association with cognitive dysfunction in these patients. CONCLUSIONCognitive dysfunction is a frequent finding in patients with schizophrenia. Future research is needed to investigate the factors increasing its risk.KEYWORDSCognition; Memory; Outpatients; Pakistan; Schizophrenia; Tertiary Care Centres.

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)0.001
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 score0.043
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.335
Teacher spread0.328 · 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
Published2025
Admission routes1
Has abstractyes

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