Direct‐to‐consumer genetic testing: good, bad or benign?
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
A wide variety of genetic tests are now being marketed and sold in direct-to-consumer (DTC) commercial transactions. However, risk information revealed through many DTC testing services, especially those based on emerging genome wide-association studies, has limited predictive value for consumers. Some commentators contend that tests are being marketed prematurely, while others support rapid translation of genetic research findings to the marketplace. The potential harms and benefits of DTC access to genetic testing are not yet well understood, but some large-scale studies have recently been launched to examine how consumers understand and use genetic risk information. Greater consumer access to genetic tests creates a need for continuing education for health care professionals so they can respond to patients' inquiries about the benefits, risks and limitations of DTC services. Governmental bodies in many jurisdictions are considering options for regulating practices of DTC genetic testing companies, particularly to govern quality of commercial genetic tests and ensure fair and truthful advertising. Intersectoral initiatives involving government regulators, professional bodies and industry are important to facilitate development of standards to govern this rapidly developing area of personalized genomic commerce.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it