Making the Decision to Participate in Predictive Genetic Testing for Arrhythmogenic Right Ventricular Cardiomyopathy
Bibliographic record
Abstract
This paper describes the experience of predictive genetic testing for Arrhythmogenic Right Ventricular Cardiomyopathy in the context of novel gene discovery. Two approaches to making the decision to engage in genetic testing were apparent: the decision to be tested either (a) develops gradually over time or (b) happens so quickly that it is felt as a "fait accompli." Six key factors that influenced the particular approach taken by the participants were identified: (1) scientific process--available and relevant predictive genetic test; (2) numerous losses or deaths within the family; (3) physical signs and symptoms of disease; (4) gender; (5) sense of relational responsibility or moral obligation to other family members; and (6) family support. This study found that at risk individuals juxtapose scientific knowledge against their experiential knowledge and the six identified factors in order to make the decision to participate in genetic testing. Recommendations include the creation of a relational space within which to provide psychological counselling and assessment for the six identified factors that shape the decision to engage in predictive genetic testing.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".