Towards a global multidisciplinary consensus framework on haemophilia gene therapy: Report of the 2nd World Federation of Haemophilia Gene Therapy Round Table
Bibliographic record
Abstract
INTRODUCTION: With approval of gene therapy for haemophilia likely in the near future, policy frameworks are needed to guide the path forward for this disruptive and novel therapeutic advance. AIM: The WFH has initiated a series of multi-stakeholder Gene Therapy Round Tables (GTRT) to better understand where guidance is needed and develop initial consensus statements to inform policy. METHODS: The first day of the 2nd GTRT was devoted to didactic presentations on models of access to gene therapy, payment and health technology assessment considerations, regulatory issues and the generation of evidence on safety and durable efficacy of gene therapy products. On the second day, participants were tasked with developing and voting on consensus statements that reflected the information presented and multi-stakeholder views expressed during discussions in the 1st and 2nd WFH GTRTs. The statements covered global access to gene therapy for all people with haemophilia (PWH), collection of long-term safety and efficacy data, ensuring gene therapy is available for all subgroups of PWH including those who have been largely excluded from clinical trials and characterizing acceptable and ideal factor expression levels for gene therapy products. RESULTS: The first 3 statements achieved consensus (at least 80% agreement) by this group of experts. The statement on identifying an ideal and an acceptable factor level expression elicited a lively discussion but failed to achieve consensus by this group. CONCLUSIONS: This issue of ideal and acceptable factor level expression and other unresolved issues will be brought to the 3rd WFH GTRT in 2020.
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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 | 0.254 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.010 | 0.022 |
| Research integrity | 0.026 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".