How the prospect of a clinical trial impacts decision-making for predictive genetic testing in amyotrophic lateral sclerosis
Bibliographic record
Abstract
Abstract Objective Genetic testing practices are rapidly evolving for people living with, or at-risk for, amyotrophic lateral sclerosis (ALS), due to emerging genotype-driven therapies. This study explored how individuals at-risk for familial ALS (fALS) perceive the opportunity to participate in a clinical trial, and to better understand how that may influence the decision-making process for predictive genetic testing. Methods This study used both quantitative and qualitative data analyses. Data were collected through an online questionnaire, followed by semi-structured interviews conducted with twelve (n=12) individuals at-risk for either SOD1- or C9orf72 -ALS who had predictive testing prior to study participation. Interview data were analyzed using reflexive thematic analysis. Results Three overarching themes were conceptualized from the data: i) the psychosocial impact of fALS; ii) perspectives of at-risk individuals on research involvement; and iii) predictive genetic counselling and testing considerations. These results contribute perspectives of the lived experience to inform predictive genetic counselling and testing practices for individuals at-risk for fALS. Conclusion Individuals at-risk for fALS view potential participation in a presymptomatic clinical trial as an actionable measure that may increase their desire for predictive genetic testing. Genetic counselling was identified as a critical component of the predictive testing process given the life-changing implications associated with a positive result. Increased access to genetic counselling, and in a timely manner, is a significant need in the ALS population given potential access to gene-specific therapies in the presymptomatic stage.
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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 | 0.072 | 0.149 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".