Recommended approaches to sharing individual research results in Alzheimer's disease research: A multidisciplinary expert Delphi consensus
Bibliographic record
Abstract
) have gained heightened value in research, and, notably, increased personal significance for participants.ObjectiveTo identify recommended approaches for sharing individual research results with participants in AD/ADRD research and determine expert consensus on best practices for sharing individual research results to participants in AD/ADRD research.MethodsThis online, modified Delphi study consisted of four rounds of surveys conducted with Alzheimer's disease research experts, including neurologists, ethicists, neuropsychologists, geneticists, clinical trialists, and other research stakeholders. The Delphi survey was informed by a targeted literature review of previously published recommendations on sharing individual research results in AD/ADRD research. A total of 81 experts were surveyed across all rounds, ranking statements on a 7-point Likert scale and providing feedback in short answer responses. After each round, feedback reports were shared to inform subsequent responses. Proportion of agreement and qualitative feedback were analyzed, with consensus defined as 75% or greater agreement.Results41 initial statements were evaluated and refined based on consensus and feedback. Concluding the final round (round 3), consensus (≥75% agreement) was achieved on 25 statements, resulting in a set of recommendations related to: study design, clinical relevance, results sharing processes, communication and understanding of results, counseling and support, and follow-up.ConclusionsThe findings of this online, modified Delphi study provide a foundation for developing standardized, ethically grounded practices for returning individual research results in Alzheimer's disease studies.
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 | 0.298 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".