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Record W2938634307 · doi:10.1038/s41591-019-0424-4

Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial

2019· article· en· W2938634307 on OpenAlex
Jordi Rodón, Jean‐Charles Soria, Raanan Berger, Wilson H. Miller, Eitan Rubin, Aleksandra Kugel, Apostolia M. Tsimberidou, Pierre Saintigny, Aliza Ackerstein, Irene Braña, Yohann Loriot, Mohammad Afshar, Vincent A. Miller, Fanny Wunder, Catherine Bresson, Jean-François Martini, Jacques Raynaud, John Mendelsohn, Gerald Batist, Amir Onn, Josep Tabernero, Richard L. Schilsky, Vladimir Lazar, J. Jack Lee, Razelle Kurzrock

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNature Medicine · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsMcGill UniversityQuebec - Clinical Research Organization in CancerJewish General Hospital
FundersNational Cancer Institute
KeywordsMedicineInternal medicineOncologyPrecision medicineClinical endpointMultivariate analysisTranscriptomePersonalized medicineClinical trialCancerDiseaseBioinformaticsGenePathologyGene expressionBiologyGenetics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.398

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 head0.007
GPT teacher head0.282
Teacher spread0.275 · 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