Enrichment factors for clinical trials in mild‐to‐moderate Alzheimer's disease
Bibliographic record
Abstract
Abstract Introduction Heterogeneity of outcomes in Alzheimer's disease (AD) clinical trials necessitates large sample sizes and contributes to study failures. This analysis determined whether mild‐to‐moderate AD populations could be enriched for cognitive decline based on apolipoprotein ( APOE ) ε4 genotype, family history of AD, and amyloid abnormalities. Methods Modeling estimated the number of randomized patients needed to detect a 2‐point treatment difference on the AD Assessment Scale–Cognitive subscale using placebo data from three randomized, double‐blind trials ( ClinicalTrials.gov Identifiers: NCT01955161 , NCT02006641 , and NCT02006654 ). Results An 80% power to detect a 2‐point treatment effect required the randomization of 148 amyloid‐positive patients; 178 ε4 homozygous or amyloid‐positive patients; and 231 ε4 homozygous, family history‐positive, or amyloid‐positive patients, compared with 1619 unenriched patients (per arm). Discussion Enrichment in mild‐to‐moderate AD clinical trials can be achieved using combinations of biomarkers/risk factors to increase the likelihood of observing potential treatment effects.
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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 | 0.336 | 0.524 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".