Utility of a Screening Test (MoCa) to Predict Amyloid Physiopathology in Mild Cognitive Impairment
Bibliographic record
Abstract
Introduction: The MoCa (Montreal Cognitive Assessment) Screening test has become relevant in recent years in the screening of patients with Mild Cognitive Impairment (MCI). It is important to seek and study simple and reliable tools in clinical practices that correlate with biological markers that have been used to predict conversion from MCI to AD. Objective: To analyze the MOCA and its cognitive sub-scores and the relationship with Amyloid pathophysiology in Alzheimer’s Disease. Methodology: 32 patients with MCI were studied, they were separated according positive (n: 20) and negative (n: 12) underlying amyloid pathology. The patients performed a extensive cognitive assessment that included MoCa Test. Results: MoCa Total Scores showed significantly different results between groups (p <0.001) as well as the Memory Score (MoCa MIS), the Executive (MoCa EIS), the Attentional Score (MoCa AIS)) (p < 0.001) and the Orientation Score (MoCa OIS)) (p < 0.05) with worse performance of patients with amyloid pathophysiology. Score of MoCa a cut-off point of < 24 was established, since the diagnostic sensitivity at this point was 83% and the specificity 70%. Conclusions: The MoCa is a useful tool to differentiate biomarker status in MCI. Future studies should study this tool in the prodromal phases of the disease.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".