CLINICAL PHENOTYPING OF THE COGNITIVELY UNIMPAIRED IN THE AD SPECTRUM USING PET IMAGING
Bibliographic record
Abstract
Abstract Clinical phenotyping of cognitively unimpaired (CU) older adults who may progress to dementia is a pressing issue in Alzheimer’s disease (AD) since early interventions are the most effective. Out-patient cognitive screening commonly uses rapid and simple tests such as the Montreal Cognitive Assessment (MoCA) but its role in preclinical AD remains unclear. We hypothesize that sub-categorizing CU based on the presence of amyloid (A) and/or tau (T) pathologies, as measured by PET, can cognitively phenotype CU using MoCA. We studied 147 CU [CDR global score 0] older adults with MK6240 and Flortaucipir tau-PET and PiB or NAV4694 amyloid PET from the ongoing HEAD study. Based on tracer uptake, individuals were divided into A-T-, A-T+, A+T- and A-T- groups. They were evaluated using the UDS-3 battery. One-way ANOVA with Tukey’s correction for multiple comparisons, was used to test for significant differences between groups. With both MK6240 and Flortaucipir, MoCA total scores revealed significant differences between A+T+ and A-T- groups [mean: -1.74 (SE: 0.56), p: 0.013 and mean: -1.56 (SE: 0.6), p: 0.049] respectively. A+T- vs A+T+ was also significant with MoCA total and MoCA Delayed Recall using MK6240 [mean: -2.05 (SE: 0.77), p: 0.041 and mean: -1.15 (SE: 0.42), p: 0.033]. MK6240 shows more sensitivity than Flortaucipir. Therefore, MoCA, a simple and commonly used in-office neuropsychological test can detect cognitive dysfunction in Aβ and tau positive (A+/T+) CU older adults. This highlights the value of precisely characterizing preclinical AD subtypes that are more likely to develop AD dementia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".