Bibliographic record
Abstract
Clinical trials in persons with dementia bring into focus the ethical dilemmas frequently confronting the clinician-scientist. Despite the existence of various ethical guidelines, most with common underlying principles, few are specific to dementia. A particular difficulty is finding a balance between respect for the autonomy of the individual and the protection of vulnerable persons, while at the same time defining an acceptable risk/benefit ratio for the study. The availability of symptomatic treatments for Alzheimer's disease also now make it difficult to argue that withholding treatment from those in the placebo arm of a clinical trial fulfills one's duty to provide best care. Those conducting clinical trials must be knowledgeable about existing legislation and ethical guidelines in order to justify to themselves and others, the design of clinical trials and their risks. They must be prepared to educate patients and family members about dementia and research, determine each potential subject's competence to consent, and ensure that decisions about participation are in accordance with the best interests of the subject. Ethical conduct of clinical trials of new antidementia therapies will require that everyone involved understands the values and beliefs that guide their decision-making and the potentially conflicting roles facing the clinician-scientist.
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 | 0.060 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".