Are large simple trials for dementia prevention possible?
Bibliographic record
Abstract
New trials of dementia prevention are needed to test novel strategies and agents. Large, simple, cardiovascular trials have successfully discovered treatments with moderate but worthwhile effects to prevent heart attack and stroke. The design of these trials may hold lessons for the dementia prevention. Here we outline suitable populations, interventions and outcomes for large simple trials in dementia prevention. We consider what features are needed to maximise efficiency. Populations could be selected by age, clinical or genetic risk factors or clinical presentation. Patients and their families prioritise functional and clinical outcomes over cognitive scores and levels of biomarkers. Loss of particular functions or dementia diagnoses therefore are most meaningful to participants and potential patients and can be measured in large trials. The size of the population and duration of follow-up needed for dementia prevention trials will be a major challenge and will need collaboration between many clinical investigators, funders and patient organisations.
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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 | 0.486 | 0.632 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".