Investigating the Association Between Verbal Forgetting and Pathological Markers of Alzheimer’s and Lewy Body Diseases
Bibliographic record
Abstract
BACKGROUND: The percentage of verbal forgetting (VF%) measure of the Rey Auditory Verbal Learning Test (RAVLT) has been proposed to differentiate patients diagnosed clinically with Alzheimer's disease (AD) and dementia with Lewy bodies (DLB). OBJECTIVE: To determine if VF% aligns with gold-standard biomarker and autopsy evidence of AD and DLB neuropathology. METHODS: Clinical, cognitive, sociodemographic, and biomarker data were collected from 315 patients with baseline cognitive impairment and 485 normal controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI). AD markers included reduced cerebrospinal fluid (CSF) amyloid-β, elevated total-tau and phosphorylated-tau, hippocampal atrophy, and the presence of amyloid plaques and neurofibrillary tangles at autopsy. DLB markers included reduced CSF α-synuclein, preserved hippocampus, atrophied putamen, occipital glucose metabolism, and the presence of Lewy bodies at autopsy. Cognitively impaired participants were classified as ADVF% (n = 190) or DLBVF% (n = 125) based on their RAVLT VF% scores using a 75% cut-off (≥75% = ADVF%, <75% = DLBVF%). Postmortem data were available for 13 ADVF% participants, 13 DLBVF% patients, and six healthy controls. RESULTS: ADVF% and DLBVF% participants did not differ on CSF or neuroimaging biomarkers, with the exception of total tau levels which were higher in ADVF%. In the subset of participants with autopsy data, comorbid AD and DLB pathology was most frequent in ADVF% participants, and pure DLB pathology was most frequent in DLBVF% participants, however, these differences were not statistically significant. CONCLUSION: The RAVLT VF% measure does not reliably align with AD and DLB neuropathology in ADNI participants.
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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".