A novel gene editing lexicon strategy for the haemophilia community: Research plan for development and preliminary results
Bibliographic record
Abstract
INTRODUCTION: Despite the progress in gene editing platforms like CRISPR/Cas9 with the potential to transform the standard of care for haemophilia, the language used to explain and discuss gene editing is not aligned across the haemophilia community. Here, we present the objective and rationale for developing a clear, consistent, and globally aligned gene editing lexicon to address these communication gaps. METHODS: Effectively communicating complex gene editing concepts requires a clear and consistent vocabulary. Through collaboration with a diversity of haemophilia stakeholders, our main goal is to develop an accurate, informative lexicon which avoids overpromising or highly technical terminology. Using an innovative process, representatives from several patient and scientific haemophilia organizations and select biotechnology companies will develop and refine language concepts to be tested with approximately seventy participants across the United States of America, United Kingdom, and Germany. Participants will include lived experience experts (LEEs) and haematologists. The process will be overseen by the Lexicon Steering Committee of global experts from leading scientific and patient organizations in the haemophilia and gene editing fields. RESULTS: Initial feedback provided a robust foundation and rationale for building clear, consistent language around gene editing. This lexicon development framework will allow for increased understanding across the haemophilia community, including the development of valid informed consent and shared decision-making materials. CONCLUSION: Results provide important building blocks for stimuli development and highlight the need for a novel gene editing lexicon. In the next phase, language stimuli will be tested with LEEs and haematologists to better understand audience preferences and help shape the final lexicon.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".