Validation of the Danish Addenbrooke’s Cognitive Examination as a Screening Test in a Memory Clinic
Bibliographic record
Abstract
BACKGROUND: Addenbrooke's Cognitive Examination (ACE) is a cognitive screening test developed to detect dementia. It has been validated in several countries. Validation studies have predominantly included patients with various degrees of dementia and healthy controls. OBJECTIVE: The aim of this study was to evaluate the Danish version of ACE as a screening test for early dementia in an outpatient memory clinic. Further, we wanted to investigate the ability of the ACE to discriminate patients with early Alzheimer's disease (AD) from patients with depression. METHOD: 78 patients with mild AD (MMSE >or=20), 30 non-demented patients diagnosed with depression (originally referred for evaluation of cognitive symptoms), and 63 healthy volunteers, all between 60 and 85 years of age, were included. All patients were given the ACE as a supplement to the standard diagnostic work-up. RESULTS: The cut-off points for optimal trade-off between sensitivity and specificity for ACE were 85/86 (sensitivity 0.99, specificity 0.94). When these cut-off points were applied to the group of depressive patients, the specificity dropped to 0.64, indicating a great overlap in individual test scores for demented and depressed patients. CONCLUSION: The optimal cut-off points for ACE found in this Danish study were close to what is reported in most other European studies. The great overlap in ACE scores for demented and depressed patients emphasize that test scores must be interpreted with great caution when used in diagnostic work-up.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".