Lessons from predictive testing for Huntington disease: 25 years on
Bibliographic record
Abstract
The availability of predictive genetic tests has rapidly expanded in the last two decades. We can now provide testing for a range of adult onset conditions including certain cancers, cardiac diseases, and neurological disorders. These developments have recognised benefit including determining the necessity of additional screening or preventive options, relieving uncertainty, and reproductive planning. However, despite these benefits, predictive tests raise challenges regarding the ethical delivery of genetic testing, results, and services. To respond to these challenges, predictive testing protocols, such as those for Huntington disease (HD), have required several in-person appointments, spread over several weeks or months, in order to undergo counselling, testing, and receive test results.1 Originally, these multi-step, multi-visit protocols were developed to both protect individuals from the potential for serious psychological damage from receiving increased risk results, as well as to ensure that individuals undergoing testing made a fully considered decision. In addition, incorporating …
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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 | 0.000 | 0.010 |
| 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 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".