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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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 source (direct Gemma or distilled Codex), 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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