Setting the Scene: The Evidence for Pre-clinical Change, Projections of the Impact of Intervention, and Implications for Public Health
Bibliographic record
Abstract
Alzheimer's disease has long been considered a neurodegenerative disorder of late life for which there is currently no disease-modifying treatment. This view is now being revised as increasing evidence suggests a long pre-clinical phase extending back into mid-life during which there is exposure to multiple potentially reversible risk factors. Further thought is now being given to the possibility of both early life intervention programs and development of new drug treatments focusing on the pre-dementia period. But how can the impact of such treatments be measured at this early stage since overt dementia may not be diagnosed for decades? In the four talks in this symposium, we will discuss evidence for pre-clinical change, theoretical models which have been used to project the possible impact of risk factor modification in mid-life and their integration into a future public health strategies. The development of new statistical risk models to determine the impact of such prevention measures will be outlined. We will consider the possibilities for drug development targeting the pre-clinical period before presenting the PREVENT Project and EPAD ( http://ep-ad.org/ ), a multi-million euro IMI-Horizon 2020 funded project for the development of pre-clinical proof of concept trials. Titles of the four presentations: 1. Setting the scene: the evidence for pre-clinical change, projections of the impact of intervention, and implications for public health (TCR) 2. New statistical risk models for determining the impact of prevention measures in the pre-dementia period (GMT) 3. The PREVENT Study: a prospective cohort study to identify mid-life biomarkers of late-onset Alzheimer's disease (KR) 4. The European Prevention of Alzheimer's Dementia (EPAD) Project: developing interventions for the secondary prevention of Alzheimer's dementia (CWR) Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.131 | 0.295 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.018 | 0.037 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.022 | 0.041 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".