Early Detection of Alzheimer’s Disease: Biomarkers and Cognitive Screening Tools
Bibliographic record
Abstract
Early detection of Alzheimer’s disease (AD) is essential for timely intervention, disease management, and improved quality of life. This study investigates the diagnostic accuracy of combining cognitive screening tools—Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA)—with blood-based biomarkers, including amyloid-beta 42/40 ratio (Aβ42/40) and phosphorylated tau (p-tau181), for early identification of AD. A total of 180 participants categorized as cognitively normal (CN), mild cognitive impairment (MCI), or early-stage AD were assessed. Descriptive and inferential statistics, including ANOVA, Pearson correlation, t-tests, multiple linear regression, and ROC curve analysis, were conducted using IBM SPSS and GraphPad Prism. Results revealed significant differences across diagnostic groups in both cognitive scores and biomarker levels. MoCA and p-tau181 demonstrated the highest diagnostic accuracy with AUC values of 0.947 and 0.936, respectively. Regression analysis confirmed all four indicators as significant predictors of AD diagnosis (p < 0.001). Strong correlations were observed between cognitive decline and biomarker abnormalities. These findings support a multidimensional approach that integrates cognitive and biological assessments for early Alzheimer’s detection. The use of non-invasive, scalable biomarker testing alongside cognitive tools enhances diagnostic precision and holds significant potential for implementation in clinical and community settings.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".