Neuropathologic Heterogeneity Does Not Impair Florbetapir-Positron Emission Tomography Postmortem Correlates
Bibliographic record
Abstract
Neuropathologic heterogeneity is often present among Alzheimer disease (AD) patients. We sought to determine whether amyloid imaging measures of AD are affected by concurrent pathologies. Thirty-eight clinically and pathologically defined AD and 17 nondemented patients with quantitative florbetapir F-18 (F-AV-45) positron emission tomography (PET) imaging during life and postmortem histological β-amyloid quantification and neuropathologic examination were assessed. AD patients were divided on the basis of concurrent pathologies, including those with Lewy bodies (LBs) (n = 21), white matter rarefaction (n = 27), severe cerebral amyloid angiopathy (n = 11), argyrophilic grains (n = 5), and TAR DNA binding protein-43 inclusions (n = 18). Many patients exhibited more than 1 type of concurrent pathology. The ratio of cortical to cerebellar amyloid imaging signal (SUVr) and immunohistochemical β-amyloid load were analyzed in 6 cortical regions of interest. All AD subgroups had strong and significant correlations between SUVr and histological β-amyloid measures (p μ 0.001). All AD subgroups had significantly greater amyloid measures versus nondemented patients, and mean amyloid measures did not significantly differ between AD subgroups. When comparing AD cases with and without each pathology, AD cases with LBs had significantly lower SUVr measures versus AD cases without LBs (p = 0.002); there were no other paired comparison differences. These findings indicate that florbetapir-PET imaging is not confounded by neuropathological heterogeneity within AD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".