Genomic Data and Privacy
Bibliographic record
Abstract
Genetic testing has become more widespread due to several key factors, including technological advancements, reduced costs, integration into clinical care, increased public awareness, and commercialization. Sharing genetic data offers tremendous potential for advancing healthcare and knowledge. It also raises privacy concerns that require legal, ethical, and technological measures to address. Common privacy concerns include identification of an individual or blood relative from pseudonymized/anonymized data, inferring sensitive information (e.g., diagnosis or predisposition to disease) from genetic information, informing immediate family members of genetic risks with or without appropriate consent, and worries about data being misused and/or shared with third parties without adequate controls. The rules regarding privacy and genetic data vary by country and the context in which data is generated. In the United States, 3 major groups generate genetic data: (a) clinical laboratories that provide genetic testing to guide diagnostic, prognostic, and therapeutic decisions for individualized clinical care, (b) direct-to-consumer (DTC) testing that offers information on genetic ancestry and health traits and risks, and (c) research and biobank projects that drive scientific innovation and facilitate collaboration among researchers worldwide.
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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".