Comparison of the Greek Version of the Quick Mild Cognitive Impairment Screen and Montreal Cognitive Assessment in Older Adults
Bibliographic record
Abstract
Objective: Cognitive screening instruments (CSIs) are essential for everyday practice. The Quick Mild Cognitive Impairment (Qmci) screen, a short instrument designed to identify mild cognitive impairment, was recently translated into Greek (Qmci-Gr). The present study compared its diagnostic value against the Montreal Cognitive Assessment (MoCA) screen and examined its optimal cutoffs. Method: We recruited consecutive patients aged ≥55 years that presented with cognitive complaints from two outpatient clinics in Greece. The Qmci-Gr and MoCA were completed by all patients. Furthermore, they were assessed independently with a comprehensive flexible neuropsychological battery to establish a diagnostic classification. Results: In the current study, we assessed a total of 145 patients, with a median age of 70 years; 44 were classified as having Subjective Memory Complaints (SMC) but normal cognition, 32 with MCI and 69 with dementia. The Qmci-Gr had a higher accuracy compared to the MoCA in discriminating MCI from dementia, area under the curve (AUC) of 0.81 versus 0.75, respectively; however, this finding was marginally significant (p = 0.08). Its accuracy was marginally higher for distinguishing SMC from dementia, AUC of 0.94 versus 0.89 (p = 0.03). However, Qmci-Gr presented a lower accuracy than MoCa in differentiating SMC from MCI, AUC of 0.76 versus 0.94 (p = 0.006). Conclusions: The Qmci-Gr has comparable diagnostic accuracy to the MoCA regarding MCI and dementia groups. Further research, with larger and more diverse samples, may be necessary to ensure generalizability.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".