Screening Tests for Cognitive Impairment in Elderly Thai Adults: A Systematic Review
Bibliographic record
Abstract
Background: Elderly adults in Thailand are expected to represent 20% of the population in 2021. Screening tools are crucial in initiating cognitive assessments of elderly adults. Clinicians and researchers should select the tools best suited for the characteristics of their population. Several screening tools have been studied in elderly Thai adults over the past 30 years. Objective: To review the data on the screening tests for cognitive impairment currently available in Thailand, and to assess their respective strengths and issues. Materials and Methods: Seven electronic databases including MEDLINE, Embase, PsycINFO, Scopus, Google Scholar, and two specializing in Thai journals, which are ThaiJo and TDC-ThaiLIS, were searched. A hand-search of the reference lists was also undertaken. Two reviewers independently screened the articles, assessed their quality using the QUADAS-2 checklist, and extracted relevant data. Any discrepancies were resolved through discussion. Results: Twenty-eight studies assessing 33 screening tests were included. The tests were categorized into three groups, multiple-task, single-task, and questionnaire-based tools. Six articles studied their accuracy in community-based populations, while the rest were conducted at tertiary-care centers. The highest sensitivities for dementia detection were demonstrated by the Chula Mental Test for the multiple-task assessment test, and the Clock-Drawing Test for the single-task cognitive test. Conclusion: Various screening tests for cognitive impairment have been examined in the Thai population. The present study main observation was that many researchers did not clearly address their methodology and biases. Tackling these issues will ensure a high-quality methodology and validity of screening tests. Future studies should focus on either developing appropriate tools or adapting the existing tools to better suit elderly Thai adults. Keywords: Aging; Cognition; Cognitive Test; Dementia; Screening
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".