The elusive ideal of inclusiveness: lessons from a worldwide survey of neurologists on the ethical issues raised by whole-genome sequencing
Bibliographic record
Abstract
The anticipation of ethical issues that may arise with the clinical use of genomic technologies is crucial to envision their future implementation in a manner sensitive to local contexts. Yet, populations in low- and middle-income countries are underrepresented in studies that aim to explore stakeholders' perspectives on the use of such technologies. Within the framework of a research project entitled "Personalized medicine in the treatment of epilepsy", we sought to increase inclusiveness by widening the reach of our survey, inviting neurologists from around the world to share their views and practices regarding the use of whole-genome sequencing in clinical neurology and its associated ethics. We discuss herein the compelling scientific and ethical reasons that led us to attempt to recruit neurologists worldwide, despite the lack, in many low- or middle-income countries, of access to genomic technologies. Recruitment procedures and their results are presented and discussed, as well as the barriers we faced. We conclude that inclusive recruitment remains a challenging, albeit necessary and legitimate, endeavour.
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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 | 0.108 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".