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Record W2901252328 · doi:10.1093/jlb/lsy025

The stem cell market and policy options: a call for clarity

2018· article· en· W2901252328 on OpenAlexafffund
Amy Zarzeczny, Harold Atkins, Judy Illes, Jonathan Kimmelman, Zubin Master, Julie M. Robillard, Jeremy Snyder, Leigh Turner, Patricia J. Zettler, Timothy Caulfield

Bibliographic record

VenueJournal of Law and the Biosciences · 2018
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsUniversity of AlbertaUniversity of British ColumbiaSimon Fraser UniversityOttawa HospitalUniversity of OttawaNeuroDevNetMcGill UniversityUniversity of Regina
FundersHealth CanadaOntario Institute for Regenerative MedicineUniversity of TorontoMultiple Sclerosis SocietyMultiple Sclerosis Society of CanadaCanadian Institutes of Health ResearchCanadian Medical AssociationStem Cell Network
KeywordsCLARITYContext (archaeology)Psychological interventionRegenerative medicineEnthusiasmStem cellRisk analysis (engineering)MedicineBusinessEngineering ethicsPsychologyBiologyEngineering

Abstract

fetched live from OpenAlex

The field of regenerative medicine is widely viewed as having the potential to improve treatment options for a broad range of conditions. Stem cell research in particular has been celebrated for its considerable clinical promise. Although measured enthusiasm surrounding this area of research is warranted, it must be balanced by patience and set in the context of a long-term perspective that is cognizant of the many steps required to bring safe and efficacious therapies to market. Creating therapeutic applications of stem cell technologies is an intricate process involving complex biology. It will require careful scientific investigation and evaluation under responsible ethical frameworks and regulatory standards in order to safely maximize their potential. Alongside the many promising avenues of responsible research currently underway in countries throughout the world, a global market has emerged where a wide range of putative stem cell-based interventions are sold on a direct-to-consumer basis and marketed over the internet.1,2,3,4

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.708
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
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 head0.027
GPT teacher head0.325
Teacher spread0.298 · 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.

Study designTheoretical or conceptual
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

Citations22
Published2018
Admission routes2
Has abstractyes

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