Familial Communication of Research Results: A Need to Know?
Bibliographic record
Abstract
Research now provides participants greater indications of genetic risk for disease, even for conditions incidental to the research study. Given this development, should such information also be disclosed to the family of research participants? There has been some indication at the national level that genetic risk information can be disclosed to participants' families; however, limited attention has been given to returning research results to family. Thus, we have also incorporated the discussion surrounding the disclosure of genetic risk discovered in the clinic (e.g., genetic testing). A number of important questions are examined: Should genetic research results be provided to family? Are there differences between clinical and research findings that would prevent research results from being disclosed to family? Who should make the disclosure, if in fact it is done at all? We conclude by noting that the return of results is increasingly accepted as technology permits the discovery of more and more medically useful data. However, debates of whether results should be returned to participants must first be settled before moving to familial disclosure.
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 | 0.131 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.012 | 0.050 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.027 | 0.025 |
| 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; 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".