Scales as outcome measures for Alzheimer's disease
Bibliographic record
Abstract
The assessment of patient outcomes in clinical trials of new therapeutics for Alzheimer's disease (AD) continues to evolve. In addition to assessing drugs for symptomatic relief, an increasing number of trials are focusing on potential disease-modifying agents. Moreover, participants with AD are being studied earlier in their course of disease. As a result, the limitations of current outcome measures have become more apparent, as has the need for better instruments. In recognition of the need to review and possibly revise current assessment measures, the Alzheimer's Association, in cooperation with industry leaders and academic investigators, convened a Research Roundtable meeting devoted to scales as outcome measures for AD clinical trials. The meeting included a discussion of methodological issues in the use of scales in AD clinical trials, including cross-cultural issues. Specific topics related to the use of cognitive, functional, global, and neuropsychiatric scales were also presented. Speakers also addressed academic and industry initiatives for pooling data from untreated and placebo-treated patients in clinical trials. A number of regulatory topics were also discussed with agency representatives. Panel discussions highlighted areas of controversy, in an effort to gain consensus on various topics.
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 | 0.069 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".