Alzheimer’s Disease Pathophysiology and Risk Factors with Amyloid Positron Emission Tomography, an Open Science Approach, and the Consideration of Environmental Exposures
Bibliographic record
Abstract
Alzheimer’s disease (AD) is the most common underlying cause of dementia but is incompletely understood. The pathophysiology of AD involves amyloid-beta plaques, neurofibrillary tangles, and cerebrovascular changes involving white matter. Risk factors including lead (Pb) exposure influence these processes. This thesis has four components related to improving the understanding of AD pathophysiology. First, amyloid positron emission tomography (PET) tracer delivery was hypothesized to be associated with white matter integrity and was demonstrated to be correlated with established biomarkers in mild cognitive impairment. Second, an open source software package for PET analysis was created to improve transparency in AD research. Third, a systematic review of case-control studies of Pb measurement in AD is presented, which highlights the possible connection but identifies a need for studies that address early-life Pb exposure. And fourth, a hypothesis that environmental microdose lithium may mitigate Pb toxicity including cognitive impact is outlined with several literature reviews.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".