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Record W2907044034 · doi:10.1111/hae.13673

The 1st <scp>WFH</scp> Gene Therapy Round Table: Understanding the landscape and challenges of gene therapy for haemophilia around the world

2019· review· en· W2907044034 on OpenAlexaff
Glenn F. Pierce, Donna Coffin

Bibliographic record

VenueHaemophilia · 2019
Typereview
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsCanadian Hemophilia Society
Fundersnot available
KeywordsHaemophiliaMedicineGenetic enhancementRound tableClinical trialGeneSession (web analytics)GeneticsPediatricsInternal medicineBusiness

Abstract

fetched live from OpenAlex

In this first in a series of round table meetings, the 1st World Federation of Hemophilia Gene Therapy Round Table was convened to initiate a global dialogue on the expected challenges and opportunities that a disruptive therapy, such as gene therapy, will bring to the haemophilia community. Perspectives from key stakeholder groups, including healthcare professionals, regulators, payors, people with hemophilia and pharmaceutical industry representatives, were sought in the identification of the key issues we expect to face. Didactic presentations and open discussion covered the clinical development of gene therapy in haemophilia; regulatory perspectives of gene therapy; making informed decisions; accessibility, affordability and pricing of gene therapy; and ethical issues of gene therapy clinical trials. These were followed by small group work. This manuscript outlines the key issues identified and the path forward.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0110.003

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.

Opus teacher head0.230
GPT teacher head0.373
Teacher spread0.143 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations41
Published2019
Admission routes1
Has abstractyes

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