Mini-Addenbrooke’s Cognitive Examination (MACE): a Useful Cognitive Screening Instrument in Older People?
Bibliographic record
Abstract
BACKGROUND: The Mini-Addenbrooke's Cognitive Examination (MACE) is a recently described brief cognitive screening instrument. OBJECTIVE: To examine the test accuracy of MACE for the identification of dementia and mild cognitive impairment (MCI) in a cohort of older patients assessed in a neurology-led dedicated cognitive disorders clinic. METHODS: Cross-sectional assessment of consecutive patients with MACE was performed independent of the reference standard diagnosis based on clinical interview of patient and, where possible, informant and structural brain imaging, and applying standard clinical diagnostic criteria for dementia and MCI. Various test accuracy metrics were examined at two MACE cut-offs ( ≤ 25/30 and ≤ 21/30), comparing the whole patient cohort with those aged ≥ 65 or ≥ 75 years, hence at different disease prevalences. RESULTS: Dependent upon the chosen cut-off, MACE was either very sensitive or very specific for the identification of any cognitive impairment in the older patient cohorts with increased disease prevalence. However, at both cut-offs the positive predictive values and post-test odds increased in the older patient cohorts. At the more sensitive cut-off, improvements in some new unitary test metrics were also seen. CONCLUSION: MACE is a valid instrument for identification of cognitive impairment in older people. Test accuracy metrics may differ with disease prevalence.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".