Operationalising a Haemophilia Gene Editing Lexicon for Practical Use
Bibliographic record
Abstract
INTRODUCTION: Gene editing therapies offer the possibility of substantial improvement in treatment and quality of life for people with haemophilia (PWH) in a landscape of dynamic therapeutic advancement. Developing a common and understandable language to discuss gene editing will be essential to ensure these treatments can be deployed in a safe and effective manner with fully informed and shared decision-making between healthcare professionals (HCPs) and PWH. A lexicon explaining and clarifying key concepts is one potential tool to address these aims. Here we evaluate how a gene editing lexicon could be deployed to maximise impact and improve patient outcomes. AIM: To operationalise the gene editing lexicon for successful adoption by the haemophilia community. METHODS: Through an innovative, iterative process, representatives from the haemophilia community, including multidisciplinary HCPs, PWH, and caregivers, with support from language strategy experts, developed a gene editing lexicon and evaluated operational aspects for real-world adoption of this resource. RESULTS: A gene editing lexicon was developed, including infographics illustrating key concepts. Infographics were adapted from the lexicon to further clarify and communicate these concepts. Infographics were found to be a potentially vital tool for enhancing the practical use of the lexicon to promote shared decision-making and attain informed consent for gene editing therapies. CONCLUSION: A gene editing lexicon shows promise for improving the understanding of gene editing for all stakeholders in the haemophilia community. Ensuring the lexicon remains up to date with current therapies and appropriate strategies for adoption such as infographics will enable this resource to have maximum impact.
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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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| 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".