Clinical and multimodal biomarker correlates of ADNI neuropathological findings
Bibliographic record
Abstract
BACKGROUND: Autopsy series commonly report a high percentage of coincident pathologies in demented patients, including patients with a clinical diagnosis of dementia of the Alzheimer type (DAT). However many clinical and biomarker studies report cases with a single neurodegenerative disease. We examined multimodal biomarker correlates of the consecutive series of the first 22 Alzheimer's Disease Neuroimaging Initiative autopsies. Clinical data, neuropsychological measures, cerebrospinal fluid Aβ, total and phosphorylated tau and α-synuclein and MRI and FDG-PET scans. RESULTS: Clinical diagnosis was either probable DAT or Alzheimer's disease (AD)-type mild cognitive impairment (MCI) at last evaluation prior to death. All patients had a pathological diagnosis of AD, but only four had pure AD. A coincident pathological diagnosis of dementia with Lewy bodies (DLB), medial temporal lobe pathology (TDP-43 proteinopathy, argyrophilic grain disease and hippocampal sclerosis), referred to collectively here as MTL, and vascular pathology were present in 45.5%, 40.0% and 22.7% of these patients, respectively. Hallucinations were a strong predictor of coincident DLB (100% specificity) and a more severe dysexecutive profile was also a useful predictor of coincident DLB (80.0% sensitivity and 83.3% specificity). Occipital FDG-PET hypometabolism accurately classified coincident DLB (80% sensitivity and 100% specificity). Subjects with coincident MTL showed lower hippocampal volume. CONCLUSIONS: Biomarkers can be used to independently predict coincident AD and DLB pathology, a common finding in amnestic MCI and DAT patients. Cohorts with comprehensive neuropathological assessments and multimodal biomarkers are needed to characterize independent predictors for the different neuropathological substrates of cognitive impairment.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".