How Can We Improve Transfer of Outcomes from Randomized Clinical Trials to Clinical Practice with Disease-Modifying Drugs in Alzheimer's Disease?
Bibliographic record
Abstract
BACKGROUND: Randomized clinical trials (RCTs) for putative disease-modifying drugs in Alzheimer's disease (AD) are using cognitive outcomes, such as the Alzheimer's Disease Assessment Scale--cognitive subscale, activities of daily living scales, such as the Alzheimer's Disease Cooperative Study Activities of Daily Living, and time from mild cognitive impairment to AD dementia. OBJECTIVE: It was the aim of this study to build clinically relevant outcomes for future use in clinical practice into RCT designs and help third-party payers to measure benefit. METHODS: We used a literature review for analysis. RESULTS: The Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) appears to be the most reliable primary outcome for RCT at different stages of AD, with the Relevant Outcome Scale for Alzheimer's Disease (ROSA) as a suitable alternative. The importance of current AD biomarkers vis-à- vis determination of efficacy of disease-modifying drugs has yet to be established; however, it is likely that at least one amyloid-specific test will be required prior to treatment with a drug acting predominantly on β-amyloid (Aβ42). Furthermore, serial MRI may be required to monitor adverse side effects associated with such drugs. CONCLUSIONS: Global clinical scales such as CDR-SB and ROSA should be considered for use with treatments aiming at slowing disease progression.
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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 | 0.393 | 0.667 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 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".