Bibliographic record
Abstract
This book was compiled to aid the interpretation of previously done dementia trials and provide assistance in creating meaningful future ones. The volume starts with a brief history of work done to date and proceeds through various clinical trial designs. A considerable amount of time is spent on different potential outcome variables, including a discussion of the representation of these outcomes via scale. Ethical considerations in treatment of dementia as well as in conducting dementia research are also discussed. Trial Designs and Outcomes in Dementia Therapeutic Research, which was edited by 2 Canadian experts on Alzheimer's disease and geriatric medicine, is well organized and indexed. Topics of interest can be easily located. It has useful discussions of different study designs that can be employed in dementia research and the types of bias that can be introduced. Chapters concerning the currently available treatments for dementia, both pharmacologic and psychosocial, are useful reviews now but will very likely rapidly become out of date and contribute little to the overall purpose of the book. The book concludes with the recognition that, while progress has been made in the treatment of dementia, the outcomes wanted by patients—a return to former functioning—are, for the most part, unavailable. Included is an impassioned discussion of what the goals of dementia research should be and how to measure them and consideration of the different aims of different stakeholders. The complexity of dementia and its impact on the elements of personhood, family, caregivers, and the health care system are stressed.
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 | 0.033 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".