A call for utilizing various screening tools in dementia diagnosis: A systematic review
Bibliographic record
Abstract
Abstract Background Dementia is a disorder distinguished by progressive and irreversible global cognitive impairment. About 45 million live with dementia globally, with an estimated increase to 75 million by 2030. Early recognition and diagnosis of dementia could enhance the efficiency of health care and quality of life. Hence, Medicare implemented a covered visit for assessing cognitive function for eligible patients as of January 2021. Previous studies described many tests and screening tools that clinicians can use to diagnose dementia. However, choosing a suitable test is solely left for clinicians to decide. We conducted a systematic review to provide an evidence‐based screening tool guide to facilitate dementia diagnosis. Method A systematic review of studies published between 2010 and 2020 in English that targeted older patients. The search included multiple global databases; Cambridge Core, Cochrane Library, Google Scholar, PubMed, and Wiley online library. Keywords: “Dementia” “Screening” “Older people” “General Hospital” and “Inpatient''. Seven independent reviewers checked the studies to avoid bias. Result 32 articles met the review criteria. More than ten dementia screening tools and tests were identified in the various clinical settings. However, the Mini‐Mental State Examination (MMSE) is the leading test with three times usage compared to other tests, followed by Montreal Cognitive Assessment (MoCA). Conclusion The Mini‐Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) are the commonly used tools in diagnosing dementia. The review revealed other tests with a higher potential for early detection of dementia in various clinical settings. Thus, this is a call for clinicians to benefit and diversify the tools in diagnosing dementia and enrich the evidence‐based research with more confirmatory studies.
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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 | 0.063 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".