Examining a Preclinical Alzheimer’s Cognitive Composite for Telehealth Administration, the tPACC, for Reliability between In-Person and Remote Cognitive Testing (P6-6.002)
Bibliographic record
Abstract
Objective: To evaluate the concordance of tPACC scores from in-person and remote testing. Background: The clinical trial landscape in Alzheimer’s disease (AD) and related dementias is focused on targeting individuals in preclinical stages. The preclinical Alzheimer’s cognitive composite (PACC) was developed for in-person administration to capture subtle cognitive decline in amyloid positive individuals compared to amyloid negative. It is desirable to have a transportable, composite measurement sensitive to detecting cognitive changes across cognitively normal (NC) and impaired (MCI and dementia) participants. Design/Methods: We examined in-person cognitive data from 662 adults (70.2±8.1y) from the Wake Forest AD Research Center’s Clinical Core, who received clinical evaluation, cognitive testing, and adjudication. The PACC in-person only was calculated using RAVLT Delayed Recall, Digit Symbol Coding (DSC), semantic fluency, Craft Story Delayed Verbatim, and MMSE total scores using baseline NC participants as a reference. The tPACC used measurements that were available at both in-person and remote visits with modifications from the in-person PACC; Montreal Cognitive Assessment (MoCA) was used in place of MMSE, and DSC was not included. A subset analysis with PACC in-person and tPACC remote from a pilot study examined reliability between in-person and remote testing. We performed correlation analysis and generated Bland-Altman plots. Results: Of the 662 adults studied, 434 (66%) were female, 522 (79%) were White, 206 (34%) were APOE4 carriers, and mean education was 15.9±4.1 years. At baseline, there is a significant positive relationship between in-person tPACC and PACC (Overall group; r2=0.94, p=<0.0001, n=640). Although there is a good agreement in both subgroups (Impaired: r2=0.86, p=<0.0001, n=301 and NC: r2=0.90, p=<0.0001, n=328), tPACC overestimates cognitive performance compared to PACC for those with lower scores. We also found good agreement between in-person PACC and remote tPACC (r2=0.83, p=<0.0001, n=38). Conclusions: There is generally good agreement between tPACC and PACC for NC and impaired individuals. Disclosure: Miss Duran has nothing to disclose. The institution of Ms. Gaussoin has received research support from NIH. The institution of Dr. Lockhart has received research support from NIH. Dr. Rundle has nothing to disclose. The institution of Dr. Espeland has received research support from Alzheimer’s Association. The institution of Dr. Espeland has received research support from National Institutes of Health. Dr. Espeland has received personal compensation in the range of $500-$4,999 for serving as a Consultant with National Institutes of Health. Dr. Williams has nothing to disclose. Tim Hughes has nothing to disclose. Suzanne Craft has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Cognito Therapeutics. Suzanne Craft has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for T3D Therapeutics. Suzanne Craft has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Cyclerion Therapeutics. Suzanne Craft has received personal compensation in the range of $500-$4,999 for serving as a member, Board of Scientific Counselors with NIA. Bonnie Sachs has received research support from NIH. Bonnie Sachs has received research support from Alzheimer’s Association. The institution of Dr. Bateman has received research support from NIA.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".