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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".