P4–161: Validation of a Peruvian version of the Memory Alteration Test administered to people with amnestic mild cognitive impairment and early‐stage Alzheimer's disease in Lima, Peru
Bibliographic record
Abstract
Due to a very high prevalence of dementia (7%) and mild cognitive impairment (9%) in Latin America, it is essential to consider a rapid and meaningful diagnostic tool that will allow us to detect between amnestic mild cognitive impairment (aMCI) and early stages of Alzheimer's disease (AD) in older populations in our country. These diagnostic tools could further be used by trained personal in the principal health institutions. 45 patients with aMCI based in the Petersen criteria, 90 patients with AD (Global Deterioration Scale: GDS; GDS4=60, GDS-5=25 and GDS-6=5) according to National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria, and 180 no-demented (ND) control volunteers were tested. The average score was significantly different between each group: ND=44.9 (DS:2.9), aMCI=30.2 (DS:3.1) and AD=21.2 (DS:3.9). A cut off point of 36 showed a sensitivity and specificity of 100% for the aMCI diagnosis (AUC=0.98) while a cut off point of 28 showed a sensitivity of 99% and specificity of 100% for the AD diagnosis (UC=0.89). To differentiate between aMCI and AD, a cut off point of 28 showed sensitivity of 86 % and specificity of 83%. The T@M is a rapid and very useful tool to differentiate between non demented, aMCI, and AD individuals. As show, it could also be useful to discriminate between patients with AD and aMCI.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".