Comparing Approaches to Optimize Cut-off Scores for Short Cognitive Screening Instruments in Mild Cognitive Impairment and Dementia
Bibliographic record
Abstract
BACKGROUND: Although required to improve the usability of cognitive screening instruments (CSIs), the use of cut-off scores is controversial yet poorly researched. OBJECTIVE: To explore cut-off scores for two short CSIs: the Standardized Mini-Mental State Examination (SMMSE) and Quick Mild Cognitive Impairment (Qmci) screen, describing adjustments in scores for diagnosis (MCI or dementia), age (≤, >75 years), and education (<, ≥12 years), comparing two methods: the maximal accuracy approach, derived from receiver operating characteristic curves, and Youden's Index. METHODS: Pooled analysis of assessments from patients attending memory clinics in Canada between 1999-2010 : 766 with mild cognitive impairment (MCI) and 1,746 with dementia, and 875 normal controls. RESULTS: The Qmci was more accurate than the SMMSE in differentiating controls from MCI or cognitive impairment (MCI and dementia). Employing the maximal accuracy approach, the optimal SMMSE cut-off for cognitive impairment was <28/30 (AUC 0.86, sensitivity 74%, specificity 88%) versus <63/100 for the Qmci (AUC 0.93, sensitivity 85%, specificity 85%). Using Youden's Index, the optimal SMMSE cut-off remained <28/30 but fell slightly to <62/100 for the Qmci (sensitivity 83%, specificity 87%). The optimal cut-off for MCI was <29/30 for the SMMSE and <67/100 for the Qmci, irrespective of technique. The maximal accuracy approach generally produced higher Qmci cut-offs than Youden's Index, both requiring adjustment for age and education. There were no clinically meaningful differences in SMMSE cut-off scores by age and education or method employed. CONCLUSION: Caution should be exercised selecting cut-offs as these differ by age, education, and method of derivation, with the extent of adjustment varying between CSIs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".