Genotype-driven recruitment: a strategy whose time has come?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Genotype-Driven Recruitment (GDR) is a research design that recruits research participants based on genotype rather than based on the presence or absence of a particular condition or clinical outcome. Analyses of the ethical issues of GDR studies, and the recommendations derived from these analyses, are based on GDR research designs that make use of genetic information already collected in previous studies. However, as genotyping becomes more affordable, it is expected that genotypic information will become a common part of the information stored in biobanks and held in health care records. Furthermore, individuals will increasingly gain knowledge of their own genotypes through Direct-to-Consumer services. One can therefore foresee that individuals will be invited to participate not only in follow-up GDR studies but also in original GDR studies because genetic information about them is available. These individuals may or may have not participated in research before and may or may not be aware that their genetic information is available for research. DISCUSSION: From a conceptual point of view, we investigate whether the current ethics-related recommendations for the conduct of GDR suffice for a broader array of circumstances under which genetic information can be available. Our analysis reveals that the existing recommendations do not suffice for a broader use of GDR. SUMMARY: Our findings refocus attention on ethical issues which are neither new nor specific to GDR but which place greater demand on coordinated solutions. These challenges and approaches for addressing them are discussed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.015 |
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 it