Cognitive Screening of Pakistani Substance-Dependent Male Patients using Montreal Cognitive Assessment Score (MoCA)
Bibliographic record
Abstract
Objectives: The objective of this study was to determine the cognitive impairments present in Pakistani substance -dependent patients admitted in rehabilitation centers of Islamabad/Rawalpindi in a time-efficient and cost effective way.Globally, Montreal Cognitive Assessment (MoCA) is being used for cognitive screening of patients, due to its established clinical utility and ease of administration.Therefore, we sought to investigate acceptability of this test in substance -dependent patients of Pakistani origin.Study design: This is a multi-site, cross-sectional study.Methods: In this study, MoCA was administered to 100 substance dependents in-patients together with 60 age and education level matched nonsubstance using healthy controls.All the study participants were male and aged between 23-47. Results:The mean MoCA score was 18.5± 4.5 for addicted patients and 74% had abnormal score (≤ 26 value) and 29.1± 1.2 for the controls.In our study population, age and education levels were negatively associated with MoCA scores.Visuospatial processing, working memory and executive function domains were found to be significantly impaired in all the cases.Majority of our substance -dependent patients (86%) did not find cognitive screening with MoCA as an unpleasant experience.Conclusions: MoCA test can be used as an initial cognitive screening tool in Pakistani substance-dependent patients admitted in rehabilitation centers.
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 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 | 0.000 |
| 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".