Are Canadian and U.S. Based Direct-to-Consumer Genetic Testing Companies Complying with the ‘Accuracy Principle’ Outlined in PIPEDA?
Bibliographic record
Abstract
The websites of 26 Direct-to-Consumer Genetic Testing (DTC-GT) companies based in the United States and or Canada selling health-related genetic testing services to Canadian residents were analyzed to determine whether the companies are complying with the ‘Accuracy Principle’ outlined in the ‘Personal Information Protection and Electronic Documents Act’ (PIPEDA). The main web pages of the DTC-GT companies’ websites along with their Privacy Policies and Terms of Service were examined to assess the statements made about the clinical and analytical validity of their genetic tests. The findings show that the majority of the DTC-GT companies’ websites (17 of 26) made contradictory statements relating to the accuracy of their genetic tests on one or more of their website tabs as compared to their Privacy Policies and Terms of Service. The results indicate that DTC-GT companies are not clearly articulating the limits to the requirement for accuracy, and their genetic tests are not sufficiently accurate for the purposes for which they are to be used, contrary to the requirements of Principles 4.6, 4.6.1 and 4.6.3 of the ‘Accuracy Principle’ outlined in PIPEDA.
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 | 0.014 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".