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Record W4393943519 · doi:10.1158/2159-8290.cd-23-1500

The Virtual Child

2024· article· en· W4393943519 on OpenAlexafffund
Richard J. Gilbertson, Sam Behjati, Anna-Lisa Böttcher, Marianne Bronner‐Fraser, Matthew Burridge, Henrick Clausing, Harry Clifford, Tracey S. Danaher, Laura Donovan, Jarno Drost, Alexander M.M. Eggermont, Chris Emerson, Mona G. Flores, Petra Hamerlik, Nada Jabado, A. S. Jones, Henrick Kaessmann, Claudia L. Kleinman, Marcel Kool, Lena M. Kutscher, Gavin Lindberg, Emily Linnane, John C. Marioni, John M. Maris, Michelle Monje, Alexandra Macaskill, Steven Niederer, Paul A. Northcott, Elizabeth Peeters, Willemijn Plieger-van Solkema, Liane Preußner, Anne C. Rios, Karsten Rippe, Peter Sandford, Nikolaos G. Sgourakis, Adam Shlien, Pete Smith, Karin Straathof, P. J. E. Sullivan, Mario L. Suvà, Michael D. Taylor, E. Thompson, Roser Vento‐Tormo, Brandon J. Wainwright, Robert J. Wechsler‐Reya, Frank Westermann, Shannon Winslade, Bissan Al‐Lazikani, Stefan M. Pfister

Bibliographic record

VenueCancer Discovery · 2024
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsVancouver FoundationHospital for Sick ChildrenSickKids FoundationMcGill UniversityMcGill Genome CentreJewish General HospitalMcGill University Health Centre
FundersNational Cancer InstituteCanadian Institutes of Health ResearchCancer Research UK
KeywordsComputer scienceSubject (documents)Clinical trialCancerVirtual worldHuman–computer interactionMedicineBioinformaticsWorld Wide WebBiology

Abstract

fetched live from OpenAlex

SUMMARY: We are building the world's first Virtual Child-a computer model of normal and cancerous human development at the level of each individual cell. The Virtual Child will "develop cancer" that we will subject to unlimited virtual clinical trials that pinpoint, predict, and prioritize potential new treatments, bringing forward the day when no child dies of cancer, giving each one the opportunity to lead a full and healthy life.

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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.019
GPT teacher head0.319
Teacher spread0.300 · 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
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

Citations7
Published2024
Admission routes2
Has abstractyes

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