Psychometric and diagnostic properties of the Taiwan version of the Quick Mild Cognitive Impairment screen
Bibliographic record
Abstract
There is a need for a screening tool with capacities of accurate detection of early mild cognitive impairment (MCI) and dementia and is suitable for use in a range of languages and cultural contexts. This research aims to evaluate the psychometric and diagnostic properties of the Taiwan version of Qmci (Qmci-TW) screen and to explore the discriminating ability of the Qmci-TW in differentiating among normal controls (NCs), MCI and dementia. Thirty-one participants with dementia and 36 with MCI and 35 NCs were recruited from a neurology department of regional hospital in Taiwan. Their results on the Qmci-TW, Taiwanese version of the Montreal Cognitive Assessment (MoCA), and Traditional Chinese version of the Mini-Mental State Examination (MMSE) were compared. For analysis, we used Cronbach's α, intraclass correlation coefficient, Spearman's ρ, Kruskal-Wallis test, receiver operating characteristic curve analysis, and multivariate analysis, as appropriate. The Qmci-TW exhibited satisfactory test-retest reliability, internal consistency, and interrater reliability as well as a strong positive correlation with results from the MoCA and MMSE. The optimal cut-off score on the Qmci-TW for differentiating MCI from NC was ≤ 51.5/100 and dementia from MCI was ≤ 31/100. The MoCA exhibited the highest accuracy in differentiating MCI from NC, followed by the Qmci-TW and then MMSE; whereas, the Qmci-TW and MMSE exhibited the same accuracy in differentiating dementia from MCI, followed by the MoCA. The Qmci-TW may be a useful clinical screening tool for a spectrum of cognitive impairments.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".