Direct‐to‐consumer genetic testing: good, bad or benign?
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.
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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 | 0.028 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.040 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 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".