Gene therapy for hemophilia: Current status and laboratory consequences
Bibliographic record
Abstract
Since the cloning and characterization of the factor VIII (FVIII) and factor IX genes in the mid-1980s, gene therapy has been perceived as having significant potential for the treatment of severe hemophilia. Now, some 35 years later, these proposals are close to being realized through the licensing of the first clinical gene therapy product. Adeno-associated viral vector-mediated gene therapy for hemophilia A and B has been extensively investigated in preclinical models over the past 20 years, and since 2011, there has been increasing evidence in early phase clinical trials that this therapeutic strategy can provide safe and effective rescue of the hemostatic phenotype in severe hemophilia. As the uptake of hemophilia gene therapy progresses, it is clear that many aspects of the gene therapy process require crucial laboratory support to ensure safe and effective outcomes from his new therapeutic paradigm. These laboratory contributions extend from evaluations of the gene therapy vehicle, assessments of the patient immune status for the vector, and ultimately the performance of assays to determine the hemostatic benefit of the gene therapy and potentially of its long-term safety on the host genome. As with many aspects of past hemophilia care, the safe and effective delivery of gene therapy will require an informed and coordinated contribution from laboratory science.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".