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 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| 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".