Screening for Mild Cognitive Impairment: Comparison of “MCI Specific” Screening Instruments
Bibliographic record
Abstract
BACKGROUND: Sensitive and specific instruments are required to screen for cognitive impairment (CI) in busy clinical practice. The Montreal Cognitive Assessment (MoCA) is widely validated but few studies compare it to tests designed specifically to detect mild cognitive impairment (MCI). OBJECTIVE: Comparison of two "MCI specific" screens: the Quick Mild Cognitive Impairment screen (Qmci) and MoCA. METHODS: Patients with subjective memory complaints (SMC; n = 73), MCI (n = 103), or dementia (n = 274), were referred to a university hospital memory clinic and underwent comprehensive assessment. Caregivers, without cognitive symptoms, were recruited as normal controls (n = 101). RESULTS: The Qmci was more accurate than the MoCA in differentiating MCI from controls, area under the curve (AUC) of 0.90 versus 0.80, p = 0.009. The Qmci had greater (AUC 0.81), albeit non-significant, accuracy than the MoCA (AUC 0.73) in separating MCI from SMC, p = 0.09. At its recommended cut-off (<62/100), the Qmci had a sensitivity of 90% and specificity of 87% for CI (MCI/dementia). Raising the cut-off to <65 optimized sensitivity (94%), reducing specificity (80%). At <26/30 the MoCA had better sensitivity (96%) but poor specificity (58%). A MoCA cut-off of <24 provided the optimal balance. Median Qmci administration time was 4.5 (±1.3) minutes compared with 9.5 (±2.8) for the MoCA. CONCLUSIONS: Although both tests distinguish MCI from dementia, the Qmci is particularly accurate in separating MCI from normal cognition and has shorter administration times, suggesting it is more useful in busy hospital clinics. This study reaffirms the high sensitivity of the MoCA but suggests a lower cut-off (<24) in this setting.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".