Bibliographic record
Abstract
Prevention in Alzheimer's disease and other dementias (AD/dementia) is defined on the basis of clinical states and their expressed symptoms. Primary prevention refers to delaying the development of the full-blown state of clinically expressed disease in normal individuals. Current primary prevention research is driven by evidence of AD/dementia protective factors that have emerged from epidemiological studies. The first randomized controlled trials (RCTs) of primary AD/dementia prevention have been designed to test the efficacy and safety of NSAIDs, hormonal therapy, antihypertensive drugs and antioxidants. The experience of these trials has indicated safety concerns as a key issue and highlighted significant design challenges in this type of research. These trials have required large sample sizes and unsustainable costs. There should be consideration given in future trials to enriching study samples with risk factors to increase progression rates to AD/dementia. Innovative strategies will also be needed to recruit and retain subjects given the long follow-up periods, modest perceived benefit and the potential for the risk-benefit ratio to change during the trial. It is foreseeable that regulatory authorities will be presented with primary prevention RCTs for approval and labelling, and that criteria to evaluate such evidence still need to be developed.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".