Bibliographic record
Abstract
In the era of chronic disease, we are challenged to find therapies that provide symptomatic relief and ideally, alter the course of the underlying disease. In Alzheimer's disease (AD), these issues are complicated by the disease itself, which affects the subject's decision-making capacity for participation in the research. According to established ethical guidelines it is clear that individuals with impaired capacity may participate in research and their risk should be no greater than that which the individual would have in day to day activities with anticipation of benefits within that realm. Decision making processes are complex and involve proxies who themselves have biases about their loved one and the potential for participating in the research. Newer disease-modifying approaches such as immunotherapy have potential for affecting the course of the underlying disease but with greater risk of more significant side effects. Ideally the health care of the subjects is not disadvantaged by research participation. At the same time, trials of potentially riskier therapy are relevant in subjects with the disease. Research for subjects with AD must have appropriate safeguards in place to enable effective progress in innovative therapy for a vulnerable, often elderly population. Recommendations are made which could further our capacity to undertake ethical research in the AD population.
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 | 0.120 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".