Decentralised clinical trials for medications to reduce the risk of dementia: consensus report and guidance
Bibliographic record
Abstract
Abstract Background Recent growth in the functionality and use of technology has prompted an increased interest in the potential for remote or decentralised clinical trials in dementia. There are many potential benefits associated with decentralised medication trials, but the field is currently lacking specific recommendations for their delivery in the dementia field. Method A modified Delphi method engaged a panel with substantial expertise in dementia trial design and delivery and backgrounds that included neurology, psychiatry, pharmacology and psychology, to develop recommendations for the conduct of decentralised medication trials in dementia prevention. A working group of researchers and clinicians with expertise in dementia trials further refined the recommendations. Result Overall, the recommendations support the delivery of decentralised remote trials in dementia prevention provided adequate safety checks and balances are included. A total of 40 recommendations will be presented, spanning aspects of decentralised clinical trials dementia prevention trials in phases 2b‐4, including safety, dispensing, outcome assessment, and data collection. Conclusion With these recommendations we provide an accessible, pragmatic guide for the design and conduct of remote medication trials for the prevention of dementia.
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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 | 0.292 | 0.386 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.011 | 0.009 |
| Research integrity | 0.022 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".