Comparison of the Greek Version of the Quick Mild Cognitive Impairment Screen and Standardised Mini-Mental State Examination
Bibliographic record
Abstract
INTRODUCTION: Short cognitive screening instruments (CSIs) are widely used to stratify patients presenting with cognitive symptoms. The Quick Mild Cognitive Impairment (Qmci) screen is a new, brief (<5mins) CSI designed to identify mild cognitive impairment (MCI), which can be used across the spectrum of cognitive decline. Here we present the translation of the Qmci into Greek (Qmci-Gr) and its validation against the widely-used Standardised Mini-Mental State Examination (SMMSE). METHODS: Consecutive patients aged ≥55 years presenting with cognitive complaints were recruited from two outpatient clinics in Greece. All patients completed the Qmci-Gr and SMMSE and underwent an independent detailed neuropsychological assessment to determine a diagnostic classification. RESULTS: In total, 140 patients, median age 75 years, were included; 30 with mild dementia (median SMMSE 23/30), 76 with MCI and 34 with subjective memory complaints (SMC) but normal cognition. The Qmci-Gr had similar accuracy in differentiating SMC from cognitive impairment (MCI & mild dementia) compared with SMMSE, area under the curve (AUC) of 0.84 versus 0.79, respectively; while accuracy was higher for the Qmci-Gr, this finding was not significantly different, (p = .19). Similarly, the Qmci-Gr had similar accuracy in separating SMC from MCI, AUC of 0.79 versus 0.73 (p = .23). CONCLUSIONS: The Qmci-Gr compared favorably with the SMMSE. Further research with larger samples and comparison with other instruments such as the Montreal Cognitive Assessment is needed to confirm these findings but given its established brevity, it may be a better choice in busy clinical practice in Greece.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".