‘It had to be done’: genetic testing decisions for arrhythmogenic right ventricular cardiomyopathy
Bibliographic record
Abstract
Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable disease of the heart muscle, causing life-threatening ventricular arrhythmias, sudden cardiac death and/or biventricular heart failure. Little research examines ARVC genetic test decisions, despite the gravity of the condition. This qualitative study used semi-structured interviews to explore the testing decisions of 21 individuals across 15 families segregating a well-studied, particularly lethal form of ARVC caused by a p.S358L TMEM43 mutation. Genetic testing decisions were rarely described as 'decisions' per se, but rather 'something that had to be done'. This perception was attributed to personality type or personal suspicion of carrying the TMEM43 mutation, but most often was described in the context of testing for other family members, usually children. Participants related a strong need to rule out risk, more for children than for themselves, but lingering doubts remained about personal and children's risk for ARVC, even when gene test results were negative. Study findings highlight the interdependent nature of genetic test decisions and suggest that an individualistic conception of autonomy in genetic services may not meet the needs of affected families. Findings also suggest the need for follow-up support of families affected by ARVC, including for those individuals testing negative for the family mutation.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".