The epidemiology is promising, but the trial evidence is weak. Why pharmacological dementia risk reduction trials haven't lived up to expectations, and where do we go from here?
Bibliographic record
Abstract
There is an urgent need for interventions that can prevent or delay cognitive decline and dementia. Decades of epidemiological research have identified potential pharmacological strategies for risk factor modification to prevent these serious conditions, but clinical trials have failed to confirm the potential efficacy for such interventions. Our multidisciplinary international group reviewed seven high-potential intervention strategies in an attempt to identify potential reasons for the mismatch between the observational and trial results. In considering our findings, we offer constructive recommendations for the next steps. Overall, we observed some differences in the observational evidence base for the seven strategies, but several common methodological themes that emerged. These themes included the appropriateness of trial populations and intervention strategies, including the timing of interventions and other aspects of trials methodology. To inform the design of future clinical trials, we provide recommendations for the next steps in finding strategies for effective dementia risk reduction.
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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 | 0.409 | 0.650 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.020 | 0.033 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 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; 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".