Critical evaluation of COSMIN scores in scales for mild cognitive impairment and Alzheimer's disease: A comprehensive review
Bibliographic record
Abstract
Background: Timely diagnosis and intervention of mild cognitive impairment (MCI) can delay the development of Alzheimer's disease (AD). Objective: The purpose of this study was to analyze assessment tools for cognitive function using the Consensus Criteria for Selection of Health Measurement Instruments (COSMIN) method. Comparing the validity, reliability, and practicality of these assessment tools helps clinicians select appropriate assessment tools for patients, thereby improving diagnostic accuracy. Methods:We followed the updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and used the COSMIN checklist to conduct a thorough methodological quality assessment of the studies. The measurement properties were evaluated and rated on a scale from excellent to poor, based on adapted criteria. We then synthesized the best evidence by combining the COSMIN outcomes with the quality of findings to ensure a precise and comprehensive analysis. Results: We identified a total of 156 publications, which included 19 different cognitive assessment instruments. Among these, the Telephone version of the Cantonese Mini-Mental State Examination (T-CMMSE), the Montreal Cognitive Assessment (MoCA), and the Hong Kong versions of the MoCA (HK-MoCA-A1 and A2) demonstrated distinguished qualities. The assessment of measurement properties included internal consistency, reliability, validity, and sensitivity and specificity. Notably, the T-CMMSE showed superior methodological quality based on our rigorous analysis. Conclusions: The T-CMMSE, MoCA, and HK-MoCA-A1 and A2 were found to be notable cognitive assessment tools for MCI and AD. Future research should aim to expand on these findings by exploring a wider range of tools and contexts.
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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 | 0.083 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".