Improving Alzheimer's disease phase II clinical trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 it