Biomarkers and cognitive endpoints to optimize trials in Alzheimer's disease
Bibliographic record
Abstract
OBJECTIVE: To find the combination of candidate biomarkers and cognitive endpoints to maximize statistical power and minimize cost of clinical trials of healthy elders at risk for cognitive decline due to Alzheimer's disease. METHODS: Four-hundred and twelve cognitively normal participants were followed over 7 years. Nonlinear methods were used to estimate the longitudinal trajectories of several cognitive outcomes including delayed memory recall, executive function, processing speed, and several cognitive composites by subgroups selected on the basis of biomarkers, including APOE-ε4 allele carriers, cerebrospinal fluid biomarkers (Aβ 42, total tau, and phosphorylated tau), and those with small hippocampi. RESULTS: Derived cognitive composites combining Alzheimer's Disease Assessment Scale (ADAS)-cog scores with additional delayed memory recall and executive function components captured decline more robustly across biomarker groups than any measure of a single cognitive domain or ADAS-cog alone. Substantial increases in power resulted when including only participants positive for three or more biomarkers in simulations of clinical trials. INTERPRETATION: Clinical trial power may be improved by selecting participants on the basis of amyloid and neurodegeneration biomarkers and carefully tailoring primary cognitive endpoints to reflect the expected decline specific to these individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.242 | 0.361 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".