Development of a novel gene editing lexicon for hemophilia: methodology and results
Bibliographic record
Abstract
Background: Clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9)-based targeted gene editing platforms are being developed to treat genetic diseases like hemophilia. Such novel therapy involves complex concepts and terminology that require aligned language to engage key stakeholders in the hemophilia community. Thus, a globally aligned gene editing lexicon - a consistent language to communicate the fundamentals of gene editing in hemophilia, designed to be credible and accessible for people with hemophilia and caregivers while avoiding unnecessary complexity - is required to address this need. Objectives: To establish an aligned language and communications framework that facilitates informed consent and shared decision-making regarding gene editing and treatment considerations in hemophilia. Methods: = 24) informed the lexicon development, which was further validated by a steering committee of global experts in the hemophilia and gene editing fields. Finally, optimized language recommendations were developed for a clear, consistent gene editing lexicon. Results: Key themes included insights into audience mindsets, guiding language principles, and optimized terminology for key topics like gene editing concepts and posttreatment considerations. Audience mindsets revealed cautious optimism around gene therapy, with more skepticism around gene editing. Guiding language principles indicated a preference for plainspoken over technical language, definitions that link to patient benefits, and explanations that highlight the precise nature of gene editing. Conclusion: This collaborative approach ensures broad adoption of the lexicon within the hemophilia community and readiness for beta 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 | 0.038 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".