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Record W2734548565 · doi:10.1016/j.stemcr.2017.05.029

Assessing the Safety of Human Pluripotent Stem Cells and Their Derivatives for Clinical Applications

2017· article· en· W2734548565 on OpenAlexaff
Peter W. Andrews, Uri Ben‐David, Nissim Benvenisty, Peter Coffey, Kevin Eggan, Barbara B. Knowles, András Nagy, Martín F. Pera, Benjamin Reubinoff, Peter J. Rugg‐Gunn, Glyn Stacey

Bibliographic record

VenueStem Cell Reports · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersMedical Research CouncilBiotechnology and Biological Sciences Research CouncilUK Regenerative Medicine PlatformThermo Fisher Scientific
KeywordsInduced pluripotent stem cellBiologyHuman Induced Pluripotent Stem CellsEpigeneticsStem cellComputational biologyEmbryonic stem cellBioinformaticsCell biologyGeneticsGene

Abstract

fetched live from OpenAlex

Pluripotent stem cells may acquire genetic and epigenetic variants during culture following their derivation. At a conference organized by the International Stem Cell Initiative, and held at The Jackson Laboratory, Bar Harbor, Maine, October 2016, participants discussed how the appearance of such variants can be monitored and minimized and, crucially, how their significance for the safety of therapeutic applications of these cells can be assessed. A strong recommendation from the meeting was that an international advisory group should be set up to review the genetic and epigenetic changes observed in human pluripotent stem cell lines and establish a framework for evaluating the risks that they may pose for clinical use.

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)0.030
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.030
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.060
GPT teacher head0.375
Teacher spread0.315 · 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 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

Citations137
Published2017
Admission routes1
Has abstractyes

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