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Record W4405048924 · doi:10.1182/blood-2024-211444

Unwavering Priorities: Global Physician Alignment on Gene Therapy for Hemophilia

2024· article· en· W4405048924 on OpenAlexaboutno aff
Tze Yen Loo, Zulisa Saripuddin, Boey Yee Cheng, Ayse Levent

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSpecialtyFamily medicineDemographicsDemography

Abstract

fetched live from OpenAlex

Background: Gene therapy represents a novel approach to managing hemophilia, offering the potential for transformative improvements in patient outcomes. As this innovative treatment modality gains momentum, understanding the factors physicians prioritize when considering gene therapy is crucial for its successful implementation and widespread adoption. Insight into physician preferences can guide the development and refinement of gene therapy products, ensuring they align with the needs and expectations of the medical community. Aims: This study aimed to identify and compare the attributes physicians deem most important when selecting gene therapy as a treatment option for patients with hemophilia A and B. By elucidating these priorities across various physician subgroups, we sought to uncover any potential differences or consensus in decision-making criteria. Methods: The Ipsos Hemophilia Therapy Monitor, a multi-country quantitative online survey, was conducted among 259 physicians specialized in treating patients with hemophilia A and B across France (n=30), Germany (n=40), Italy (n=40), Spain (n=40), and the UK (n=26), US (n=70), and Canada (n=13). Data were collected between November 2022 and February 2023. Physicians were screened for duration of practice in their specialty and hemophilia patient caseload. Physicians were asked to rank a list of attributes related to efficacy, safety, convenience, and access in the selection of gene therapy. We compared the top-ranked attributes across different physician subgroups to provide a nuanced analysis of the physician priorities across diverse demographics. Results: Our analysis revealed a consistent prioritization of efficacy and safety-related attributes in gene therapy selection among sampled physicians, regardless of region, years of experience treating hemophilia (3-10 years, 11-20 years, 21-35 years), recent clinical trial involvement in the past 6 months, and patient caseloads. 37% of the physicians - at an aggregate level - identified ‘durable factor expression’ as their top priority, while 29% emphasized the importance of ‘predictability in gene therapy outcomes’. 23% of the physicians considered ‘long-term safety data’ the most important factor in making a gene therapy decision. Summary/Conclusion: This study demonstrates a consensus among physicians in the regions surveyed, regarding the key attributes considered when selecting gene therapy for hemophilia. The selection of durability, predictability, and safety considerations underscore the importance of these factors in the decision-making process. These findings highlight the importance of prioritizing these attributes in gene therapy development and clinical trials to ensure the needs of both physicians and patients are met. It is also worth noting that despite a consensus being shown, proportions of sampled physicians ranking these as top attributes are consistently below 40% - work may be needed to enhance familiarity with gene therapy and its attributes. Further research using comparator data is warranted.

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)0.009
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.325
Teacher spread0.295 · 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 designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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