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Record W4392094769 · doi:10.1038/s41409-024-02246-x

Remuneration of donors for cell and gene therapies: an update on the principles and perspective of the World Marrow Donor Association

2024· review· en· W4392094769 on OpenAlexfundno aff
Lina Hamad, Salmah Mahmood Ahmed, Eefke van Eerden, Suzanna M. van Walraven, Laura Machin

Bibliographic record

VenueBone Marrow Transplantation · 2024
Typereview
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsnot available
FundersCanadian Blood ServicesAnthony NolanLancaster University
KeywordsRemunerationMedicineSafeguardingIntensive care medicineFinanceBusinessNursing

Abstract

fetched live from OpenAlex

The cell and gene therapy (CGT) sector has witnessed significant advancement over the past decade, the inception of advanced therapy medicinal products (ATMPs) being one of the most transformational. ATMPs treat serious medical conditions, in some cases providing curative therapy for seriously ill patients. There is interest in pivoting the ATMP development from autologous based treatments to allogenic, to offer faster and greater patient access that should ultimately reduce treatment costs. Consequently, starting material from allogenic donors is required, igniting ethical issues associated with financial gains and donor remuneration within CGT. The World Marrow Donor Association (WMDA) established the Cellular Therapy Committee to identify the role WMDA can play in safeguarding donors and patients in the CGT field. Here we review key ethical principles in relation to donating cellular material for the CGT field. We present the updated statement from WMDA on donor remuneration, which supports non-remuneration as the best way to ensure the safety and well-being of donors and patients alike. This is in line with the fundamental objective of the WMDA to maintain the health and safety of volunteer donors while ensuring high-quality stem cell products are available for all patients. We acknowledge that the CGT field is evolving at a rapid pace and there will be a need to review this position as new practices and applications come to pass.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.750
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.041
GPT teacher head0.318
Teacher spread0.277 · 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 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

Citations9
Published2024
Admission routes1
Has abstractyes

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