Uncertainty in an era of transformative therapy for haemophilia: Addressing the unknowns
Bibliographic record
Abstract
Haemophilia is at the dawn of a new era in therapeutic management, one that can generate greater protection from bleeding and a functional cure in some individuals. Prior advances in protein engineering and monoclonal antibody technology have facilitated therapeutic options to maintain decreased risk of bleeding and less burdensome treatment. The use of gene transfer, first proposed in 1971 for monogenic diseases, is emerging as an effective long-term treatment for a variety of diseases. Transfer of functional factor VIII (FVIII) and factor IX (FIX) genes has witnessed a series of advances and setbacks since the first non-clinical experiments in animals were initiated nearly 30 years ago. More recently, multiyear therapeutic levels of FVIII and FIX activity have been achieved in human clinical trials, translated into meaningful clinical benefit and a functional cure. While clinical progress has been definitive, many questions remain unanswered as prelicensure phase 3 clinical trials are underway. These unanswered questions translate into a state of uncertainty about the known unknowns and unknown unknowns intrinsic to any new therapeutic platform. Accepting this modality as a means to functionally cure haemophilia also means accepting the uncertainty regarding the biology of viral vector-mediated gene transfer, which remains inadequately understood. Gene therapy is a far more complex biological 'drug' than small molecule and protein drugs, where manufacturing processes and the drugs themselves are now well characterized. Extent of community acceptance of uncertainty and acknowledgement of the need for an uncompromising drive for answers to the unknowns will characterize the introduction of this first generation of gene therapy for haemophilia to the wider patient population in both resource-rich and resource-poor countries.
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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 | 0.033 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".