Diagnosing Alzheimer's disease: Which dementia screening test to use in elderly Puerto Ricans with mild cognitive impairment and early Alzheimer's disease?
Bibliographic record
Abstract
Abstract Typically, Alzheimer's disease (AD) diagnosis is not made at its earliest period, for instance, at mild cognitive impairment (MCI) and early AD (E‐AD). Our study aims to demonstrate a correlation between the screening tools, including the Mini‐Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Clinical Dementia Rating (CDR), and the biological biomarkers in the cerebrospinal fluid (CSF) amyloid beta 1‐42 (Aβ42), phosphorylated tau (p‐tau) proteins and total tau (t‐tau)/Aβ42 ratio in Puerto Ricans > 55 years old with MCI and E‐AD. We evaluated 30 participants, including demographics, memory scales, and CSF biomarkers. Twenty‐eight CSF biomarkers (Aβ42, p‐tau protein, and t‐tau/Aβ42 ratio) were analyzed using the Meso Scale Discovery Platform (MSD). Associations between memory scales (MoCA, MMSE, CDR) and CSF markers were performed using Spearman rho correlation. Our study revealed a statistical association favoring a direct relationship between MMSE and MoCA with t‐tau/Aβ42 ratio in CSF (P = 0.022, P = 0.035, respectively). We found a trend toward significance with an inverse relationship with MMSE and Aβ42 (P = 0.069) and a direct relationship with MMSE and p‐tau (P = 0.098). MMSE and MoCA screening tests were identified with a statistically significant association with the CSF biomarkers, specifically t‐tau/Aβ42 ratio, in elderly Puerto Ricans with MCI and E‐AD. Puerto Ricans > 55 years old with MCI and E‐AD could be screened confidently with MMSE and MoCA for a higher likelihood of earlier detection and, thus, initiation of disease‐modifying treatment and prompt non‐pharmacological interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".