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Record W2562637446 · doi:10.1038/mt.2016.202

One Size Fits All?: Ethical Considerations for Examining Efficacy in First-in-Human Pluripotent Stem Cell Studies

2016· article· en· W2562637446 on OpenAlexaff
Michelle Habets, Johannes J. M. van Delden, Sophie L. Niemansburg, Harold Atkins, Annelien L. Bredenoord

Bibliographic record

VenueMolecular Therapy · 2016
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMarketing authorizationFood and drug administrationInvestigational device exemptionApproved drugInterimReputationMedicineBusinessDrugPolitical scienceLawPharmacologyBioinformatics

Abstract

fetched live from OpenAlex

The 1962 amendments to the Food, Drug, and Cosmetic Act of 1938 changed the drug authorization process of premarket notification to a mandatory approval system. It gave the US Food and Drug Administration (FDA) the power to refuse the marketing of a drug based not only on safety but, thereafter, also on efficacy. The requirement of substantial evidence of efficacy by adequate and well-controlled investigations conducted by experts qualified by scientific training and experience made it possible for the FDA to introduce standards in research.

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)0.620
metaresearch head score (Gemma)0.643
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.620
Threshold uncertainty score0.469

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6200.643
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.004
Science and technology studies0.0080.061
Scholarly communication0.0140.017
Open science0.0060.011
Research integrity0.0160.026
Insufficient payload (model declined to judge)0.0030.001

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 head0.118
GPT teacher head0.358
Teacher spread0.239 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations6
Published2016
Admission routes1
Has abstractyes

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