Equivalence of the FCSRT and RAVLT to detect medial Temporal lobe atrophy and tauopathy
Bibliographic record
Abstract
In AD research, word-learning tests are often used interchangeably despite using distinct learning protocols. This study verified the equivalence of the Rey Auditory Learning Test (RAVLT) and Free and Cued Selective Reminding Test (FCSRT) when investigating medial temporal lobe (MTL) changes and AD-related tau pathology. We obtained the FCSRT and RAVLT immediate and delayed free recalls from 286 participants aged 51+. We segmented MTL regions to obtain the volume and tau-PET signal using the [18F]MK-6240 tracer. Tau-PET Braak stages and plasma p-tau181, p-tau217 and p-tau231 quantifications were also acquired. Using partial correlations, we compared FCSRT to RAVLT as well as their ability to detect the cognitive status the AD biomarker results. FCSRT and RAVLT were strongly correlated to one another (R > 0.779), with similar differentiation of cognitively impaired and cognitively unimpaired individuals (AUC > 0.810). Both predicted MTL volume, MTL tau-PET accumulation, plasma p-tau and Braak stages similarly, with no significant effect size differences. For all tests, a subtle memory impairment was found at tau-PET Braak stage III, while more robust impairments were found at stage IV onward. Despite their differences, both the RAVLT and FCSRT are equivalent at detecting AD-related pathology and symptoms, suggesting that, in these contexts, they may be used interchangeably. However, these results should be interpreted with care since the sample is not representative of a global population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".