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Record W2920660555 · doi:10.1016/j.cmet.2019.02.002

GAPDH Expression Predicts the Response to R-CHOP, the Tumor Metabolic Status, and the Response of DLBCL Patients to Metabolic Inhibitors

2019· article· en· W2920660555 on OpenAlexaff
Johanna Chiche, Julie Reverso-Meinietti, Annabelle Mouchotte, Camila Rubio‐Patiño, Rana Mhaidly, Elodie Villa, Jozef P. Bossowski, Emma Proïcs, Manuel Grima-Reyes, Agnès Paquet, Konstantina Fragaki, Sandrine Marchetti, Josette Brière, Damien Ambrosetti, Jean‐François Michiels, Thierry Jo Molina, Christiane Copie‐Bergman, Jacqueline Lehmann‐Che, Isabelle Peyrottes, Frédéric Peyrade, É. de Kerviler, B Taillan, Georges Garnier, Els Verhoeyen, Véronique Paquis‐Flucklinger, Laetitia Shintu, Vincent Delwail, Céline Delpech-Debiais, Richard Delarue, André Bosly, Tony Petrella, Gabriel Brisou, Bertrand Nadel, Pascal Barbry, Nicolas Mounier, Catherine Thiéblemont, Jean‐Ehrland Ricci

Bibliographic record

VenueCell Metabolism · 2019
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsHôpital Maisonneuve-Rosemont
FundersHorizon 2020 Framework ProgrammeCanceropôle Provence-Alpes-Côte d’AzurCentre Hospitalier Universitaire de NiceEuropean CommissionCentre Scientifique de MonacoInstitut National de la Santé et de la Recherche MédicaleInstitut National Du CancerFondation de FranceLigue Contre le CancerFondation pour la Recherche MédicaleFondation ARC pour la Recherche sur le CancerAgence Nationale de la Recherche
KeywordsGlutaminolysisDiffuse large B-cell lymphomaCancer researchLymphomaCHOPMedicineOncologyBiologyInternal medicineCancerCancer cell

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.225
Teacher spread0.219 · 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 designObservational
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

Citations71
Published2019
Admission routes1
Has abstractno

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