Cognitive Deficits in Adult Schizophrenia and its Association with Clinical Factors
Bibliographic record
Abstract
Objective: To assess cognitive deficits, their severity and associated clinical factors in adult patients of Schizophrenia, and to compare them with controls in psychiatric setups of Gilgit and Rawalpindi. Study Design: Comparative cross-sectional study. Place and Duration of Study: Psychiatry Department, Combined Military Hospital, Gilgit and Armed Forces Institute of Mental Health (AFIMH), Rawalpindi Pakistan, from Sep 2023 to Jan 2024. Methodology: A total of 247 individuals (147 adult Schizophrenia patients and 100 controls) were included. The Montreal Cognitive Assessment test (MoCA) Urdu 7.1 version was used to assess the cognitive deficits in both patient (cases) and control groups. Various clinical factors were also assessed for association with cognitive deficits among cases. Results: Among the total 247 study participants, the mean age was 31.97±7.63 years. One hundred and seventy-eight (27.9%) individuals were male and 69(27.9%) were females. Average MoCA score of the control group was 27.56±3.30 and average MoCA score for the cases was 20.63±3.89, with highly statistically significant difference (p-value <0.001) between cases and controls. There was signification association of cognitive deficits with earlier age of onset of illness, smoking, current use of depot antipsychotic injections, treatment with combined and atypical antipsychotics and higher anticholinergic burden score, greater number of previous psychiatric admissions, job status (unemployment) and a family history of psychiatric Illness. Conclusion: Cognitive impairment is more frequently seen in Schizophrenia patients as compared to controls, with several cognitive domains significantly impacted including attention, concentration, executive functions and visuo-spatial abilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".