Validating screening instruments for cognitive impairment in older South Asians in the United Kingdom
Bibliographic record
Abstract
BACKGROUND: The numbers of older South Asians in the United Kingdom are rising. Investigation of their mental health has been neglected compared to their physical health. OBJECTIVES: This study aimed to determine the sensitivity and specificity of modified versions of two screening instruments for cognitive impairment (Mini-Mental State Examination and Abbreviated Mental Test) in a community-based population. DESIGN: Two-stage study comparing screening instruments against diagnostic interview. SETTING: South, central and north Manchester. SUBJECTS: Community-resident South Asians aged 60 years and over. METHODS: Subjects were approached via their general practitioners and interviewed at home. Sensitivity and specificity for the screening instruments were calculated using receiver operating characteristic (ROC) curve analysis. RESULTS: For the Gujarati population, the MMSE cutoff was >/=24 (sensitivity 100%, specificity 95%) and AMT>/=6 (sensitivity 100%, specificity 95%). For the Pakistani population, the MMSE cutoff was >/=27 (sensitivity 100%, specificity 77%) and AMT>/=7 (sensitivity 100%, specificity 87%). CONCLUSIONS: Culturally modified versions of the Mini-Mental State Examination and Abbreviated Mental Test are acceptable and may have a high degree of sensitivity. They may assist with the recognition of cognitive impairment, if an appropriate cutoff is used.
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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 | 0.008 | 0.023 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".