Early screening for mild cognitive impairment among adults aged 60 and above: A pilot study at a secondary hospital in Ho Chi Minh City, Vietnam
Bibliographic record
Abstract
Abstract Background This pilot study explored the feasibility of early screening for mild cognitive impairment (MCI) among adults aged 60 and above at a secondary-level public hospital in Vietnam, with the goal of assessing tool acceptability and implementation potential in routine care. Methods Among 113 individuals approached, 101 provided consent and 99 completed the screening. Thirty participants met the eligibility criteria and were assessed using six validated tools: the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale-15 (GDS-15), Katz Index of Independence in Activities of Daily Living (Katz Index), Lawton Instrumental Activities of Daily Living Scale (Lawton Scale), and Senior Fitness Test. Results The enrollment rate was 89.4%, with a screening completion rate of 98%. The MMSE and MoCA demonstrated acceptable internal consistency (Cronbach’s α=0.72 and 0.68, respectively). The GDS-15 (α=0.71) was reliable for depression screening, while the Lawton Scale (α=0.73) effectively assessed functional independence in instrumental activities. However, the Katz ADL showed a ceiling effect, limiting its sensitivity to early impairments. Conclusions The study confirmed the acceptability of cognitive screening procedures and their practical application in a secondary hospital setting. These preliminary findings provide a foundation for future studies on scalability and integration into routine health services.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".