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Gene therapy for hemophilia: anticipating the unexpected

2020· article· en· W3048886060 on OpenAlexaff
Glenn F. Pierce

Bibliographic record

VenueBlood Advances · 2020
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsCanadian Hemophilia Society
Fundersnot available
KeywordsGenetic enhancementMedicineGeneBioinformaticsInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

The treatment of hemophilia, which has undergone many transformative changes over the past 60 years, is poised for yet another disruptive change: the use of gene therapy to produce functional cures in some persons with hemophilia A or B. The path toward achieving subnormal to normal levels of factor VIII and factor IX activity has not been straightforward and is littered with failures over these past 25 years. Through setbacks and iteration, adeno-associated virus (AAV) proved to be a useful vector to carry the factor VIII and IX transgenes; once cassettes were optimized and dose escalation proceeded, therapeutic levels of clotting factors were achieved by several groups. Because these transgenes produce fully active proteins, breakthrough bleeding and the use of exogenous clotting factors have nearly been eliminated for most clinical trial participants when they express a sufficient amount of protein. These first-generation gene therapies, which have initiated regulatory review, will decrease or eliminate the burden of hemophilia for many of the patients who are eligible to receive them. However, many are ineligible, including those who are seropositive or cross-reactive to multiple AAV serotypes, children, those with comorbid conditions, and those who live in countries where even the most basic plasma-derived clotting factors are not reliably available. Thus, although the first-generation gene therapies will have an important impact on the burden of hemophilia, many questions remain to be answered regarding safety, durability, and reliability as this technology advance progresses toward individuals worldwide with hemophilia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.350
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

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