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Record W2380796370 · doi:10.1016/j.stemcr.2016.05.001

Setting Global Standards for Stem Cell Research and Clinical Translation: The 2016 ISSCR Guidelines

2016· article· en· W2380796370 on OpenAlexaff
George Q. Daley, Insoo Hyun, Jane F. Apperley, Roger A. Barker, Nissim Benvenisty, Annelien L. Bredenoord, Christopher K. Breuer, Timothy Caulfield, Marcelle I. Cedars, Joyce Frey-Vasconcells, Helen E. Heslop, Ying Jin, Richard Lee, Christopher McCabe, Megan Munsie, Charles E. Murry, Steven Piantadosi, Mahendra S. Rao, Heather M. Rooke, Douglas Sipp, Lorenz Studer, Jeremy Sugarman, Masayo Takahashi, Mark C. Zimmerman, Jonathan Kimmelman

Bibliographic record

VenueStem Cell Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsMcGill UniversityMcGill University Health CentreUniversity of Alberta
FundersNational Cancer InstituteNational Heart, Lung, and Blood Institutebluebird bioMerck KGaAMedical Research CouncilNational Institute for Health and Care ResearchNIHR Sheffield Biomedical Research CentreCelgene
KeywordsTranslational researchTransparency (behavior)Engineering ethicsStem cellTranslational scienceBiologyResearch integrityPolitical scienceBiotechnologyMedicinePathologyEngineering

Abstract

fetched live from OpenAlex

The International Society for Stem Cell Research (ISSCR) presents its 2016 Guidelines for Stem Cell Research and Clinical Translation (ISSCR, 2016). The 2016 guidelines reflect the revision and extension of two past sets of guidelines (ISSCR, 2006; ISSCR, 2008) to address new and emerging areas of stem cell discovery and application and evolving ethical, social, and policy challenges. These guidelines provide an integrated set of principles and best practices to drive progress in basic, translational, and clinical research. The guidelines demand rigor, oversight, and transparency in all aspects of practice, providing confidence to practitioners and public alike that stem cell science can proceed efficiently and remain responsive to public and patient interests. Here, we highlight key elements and recommendations in the guidelines and summarize the recommendations and deliberations behind them.

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)0.286
metaresearch head score (Gemma)0.370
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.714
Threshold uncertainty score0.880

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2860.370
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0100.007
Science and technology studies0.0060.022
Scholarly communication0.0200.015
Open science0.0120.017
Research integrity0.0510.046
Insufficient payload (model declined to judge)0.0050.006

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 head0.172
GPT teacher head0.472
Teacher spread0.299 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations225
Published2016
Admission routes1
Has abstractyes

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