Why Participate in an Alzheimer’s Disease Clinical Trial? Is It of Benefit to Carers and Patients?
Bibliographic record
Abstract
BACKGROUND: We explored carer motivation for seeking participation for a relative in an Alzheimer's disease (AD) clinical drug trial, to assess impressions of the value of trial participation. We also surveyed the carers of patients who did not meet study entry screening criteria to see if our conduct of the screening visit was acceptable and ethical. METHOD: A retrospective questionnaire was sent to the carers of 36 randomized participants and 22 carers of patients who did not meet study entry screening criteria for an AD clinical treatment trial. RESULTS: Twenty-nine (81%) of the trial participant carers and 15 (68%) of carers of the group who did not meet study entry criteria returned their questionnaires with sufficient information for analysis. The prime motivators in seeking trial participation were to help their relative feel better and live longer, to contribute to medical science, to improve the health of others, and the hope of a cure. Carers of both groups found research staff supportive and would recommend trial participation to others. CONCLUSIONS: Even though trial participation is onerous and patients were generally perceived by carers as not having improved, both the screening visit and participation in the trial itself were seen as positive experiences and the expectations of carers were met.
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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 | 0.028 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".