B – 156 Predicting Alzheimer’s Neuropathology in MCI and Mild Dementia: A Comparison of Three Memory Measures
Bibliographic record
Abstract
Abstract Objective Early identification of symptoms related to Alzheimer's disease (AD), such as episodic memory deficits, enables timely intervention and monitoring. We compared the ability of three memory measures administered during early neurocognitive impairment in predicting AD at autopsy. Method Data from 831 participants in the National Alzheimer’s Coordinating Center database with a diagnosis of mild cognitive impairment (MCI; n=220) or mild dementia (Clinical Dementia Rating score of 0.5 or 1; n=611) who underwent evaluation for AD neuropathological change (ADNC) were included. The Montreal Cognitive Assessment Memory Index Score (MoCA-MIS), Benson Figure Delayed Recall (BFDR), and Craft Story Delayed Recall (CSDR) were administered at baseline. ADNC was dichotomized (i.e., none/low or intermediate/high). Paired-sample receiver operating characteristic (ROC) analyses compared the discriminant ability of the memory measures. Results The average age at baseline was 75.1 (SD=10.4), with an average of 4.1 years between assessment and death. Most participants (74%) displayed intermediate/high ADNC. All three baseline memory scores significantly differentiated the groups (AUC ps<.001), but AUC values were in the low range. The MoCA-MIS (.64) and BFDR (.68) displayed similar discriminative ability (p=.10), and both were superior to the CSDR (.58, ps<.01). Conclusion In those with MCI/mild dementia, all memory measures demonstrated low identification accuracy for ADNC. However, the MoCA-MIS and BFDR outperformed the CSDR in predicting intermediate/high ADNC at autopsy. The MoCA-MIS shows promise as a brief alternative for memory assessment, potentially supporting broader clinical applicability. Further investigation is warranted to identify the most effective brief approaches to predicting AD pathology.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".