[P‐208]: Antioxidants for the prevention of dementia: Overview of the preadvise trial
Bibliographic record
Abstract
Alzheimer's disease (AD) is a well-recognized public health concern. The primary neuropathological deficits in AD involve, accumulation of neurofibrillary tangles and senile plaques and loss of neurons, synapses, and neurotransmitters. AD therapy with anticholinesterase compounds (e.g., donepezil, galantamine, rivastigmine) and antioxidants (vitamin E) have been modest, at best. Therefore, prevention strategies may prove to be the most cost effective method for reducing the burden associated with dementia. Based on in vitro and in vivo studies, there is strong rationale for using antioxidant treatments in an effort to impede the development of dementia. Design and conduct a prevention trial using antioxidants (vitamin E and selenium) to reduce the risk of AD. As an add-on study to a NCI sponsored prostate cancer prevention trial (SELECT study), selenium and vitamin E (alone and in combination) are being investigated as potential prevention agents. Because the men in SELECT are also over the age of 50, there is a unique opportunity to assess the impact of these antioxidants on preventing dementia. The SELECT study has enrolled more than 35,500 men. The AD prevention trial (PREADVISE) was added to this trial (one year later) with the goal to recruit 5000 or more SELECT participants and monitor for AD incidence. PREADVISE uses a two-level screening process to detect dementia (Memory Impairment Screen / CERAD test battery) in more than 125 clinical centers across the U.S., Canada, and Puerto Rico. Identified dementia cases receive a standard AD evaluation. Current enrollment is 4,711, or 64% of age eligible men at participating clinics. Participant ages range from 60 to 88 years (68 median) and the sample reflects 9% African American and 10% Hispanic participants. The key to AD is prevention. There are compelling data that the brain in AD is under heightened oxidative stress, and this ongoing clinical trial is aimed at reducing oxidative damage in the ‘presymptomatic stage’ to delay or prevent the onset of AD. Recruitment and screening approaches are cost effective when large-scale prevention trials focus on more than one age-associated disease. With appropriate recruitment methods, increased enrollment of minorities is also feasible.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".