The Persian Version of the Quick Mild Cognitive Impairment Screen (Qmci-Pr): Psychometric Properties among Middle-Aged and Older Iranian Adults
Bibliographic record
Abstract
Brief cognitive screening instruments are used to identify patients presenting with cognitive symptoms that warrant further assessment. This study aimed to evaluate the reliability and validity of the Persian version of the Quick Mild Cognitive Impairment (Qmci-Pr) among middle-aged and older Iranian adults. Consecutive patients aged ≥55 years and caregivers attending with them as normal controls (NCs) were recruited from geriatric outpatient clinics and a hospital in Tehran, Iran. All patients completed the Qmci-Pr before completing an independent detailed neuropsychological assessment and staging using the Clinical Dementia Rating (CDR) Scale. NCs underwent the same assessment. In all, 92 participants with a median age of 70 years (±13) were available. Of these, 20 participants were NCs, 24 had subjective memory complaints (SMC), 24 had mild cognitive impairment (MCI), and 24 had Alzheimer’s disease (AD). The Qmci-Pr had good accuracy in differentiating SMC and NC from MCI (area under the curve (AUC): 0.80 (0.69–0.91)) and in identifying cognitive impairment (MCI and mild AD) (AUC: 0.87 (0.80–0.95)) with a sensitivity of 88% and specificity of 80%, at an optimal cut-off of <53/100. The Qmci-Pr is an accurate short cognitive screening impairment for separating NC and patients with SMC from MCI and identifying cognitive impairment. Further research with larger samples and comparison with other widely used instruments such as the Montreal Cognitive Assessment is needed. Given its established brevity, the Qmci-Pr is a useful screen for Iranian adults across the spectrum of cognitive decline.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".