Improving Alzheimer's disease phase II clinical trials
Bibliographic record
Abstract
Over the past 30 years, many drugs have been studied as possible treatments for Alzheimer's disease, but only four have demonstrated sufficient efficacy to be approved as treatments, of which three are in the same class. This lack of success has raised questions both in the pharmaceutical industry and academia about the future of Alzheimer's disease therapy. The high cost and low success rate of drug development across many disease areas can be attributed, in large part, to late-stage clinical failures (Schachter and Ramoni, Nat Rev Drug Discov 2007;6:107-8). Thus, identifying in phase II, or preferably phase I, drugs that are likely to fail would have a dramatic impact on the costs associated with developing new drugs. With this in mind, the Alzheimer's Association convened a Research Roundtable on June 23 and 24, 2011, in Washington, DC, bringing together scientists from academia, industry, and government regulatory agencies to discuss strategies for improving the probability of phase II trial results predicting success when considering the go/no-go decision-making process leading to the initiation of phase III.
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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 | 0.475 | 0.593 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".