Validity of Addenbrooke's Cognitive Examination to Discriminate between Incipient Dementia and Depression in Elderly Patients of a Private Clinic in Lima, Peru
Bibliographic record
Abstract
BACKGROUND/AIMS: Dementia and depression are different clinical conditions, but share common features, and can be indistinguishable in the initial disease stages. We aimed to establish whether the Peruvian version of the Addenbrooke's Cognitive Examination (ACE-Peru) can distinguish between the cognitive profile in patients with incipient dementia and that in patients with depression. METHODS: This was a cross-sectional study to assess the performance on the ACE-Peru of 193 elderly subjects (102 with dementia, 21 with depression, and 70 healthy controls). Depending on the diagnosis, there were two groups of cognitive impairment (CI) - the primary neurodegenerative (PN-CI) subtype and the secondary to depression (SD-CI) subtype - as well as a non-CI group. The area under the curve (AUC) of the receiver-operating characteristic curve was determined to compare the diagnostic performance, using the diagnosis of CI as the gold standard. RESULTS: In our sample of elderly subjects aged 59-82 years with at least 7 years of education, the ACE-Peru showed a significantly better performance than the MMSE (AUC = 0.997 vs. AUC = 0.887; p < 0.05) for the discrimination between PN-CI and SD-CI. CONCLUSIONS: The ACE-Peru is able to distinguish between the cognitive profile in patients with incipient dementia and that in patients with depression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".