Legal Liability and the Uncertain Nature of Risk Prediction: The Case of Breast Cancer Risk Prediction Models
Bibliographic record
Abstract
BACKGROUND: The rapidity of technological change in genetics is not always matched by the uptake of this new knowledge into practice. Increasing genetic knowledge has already led to legal liability for those who have not used it properly, such as not informing patients or their families of potential genetic risk. A similar outcome is also of concern in the case of risk prediction models used for hereditary breast cancer. RESULTS: No legal case has directly addressed the use of risk prediction models. However, as genetic medicine and risk prediction models become more widely used, the prospect of a lawsuit will also increase. Current case law is instructive on the circumstances under which medical liability actions could be pursued and circumstances under which liability is unlikely, such as the provision of faulty family history information by a patient. CONCLUSIONS: There is existing case law on family history and genetics that parallels in many respects the use of risk prediction models. However, the idea of a bad 'prediction' is a difficult legal concept. Outside of a plain misuse or failure to use a risk prediction model when circumstances clearly required it, there is little legal guidance presently available to determine the risk for medical liability.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".