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Record W2915186433 · doi:10.1126/sciadv.aav2437

Targeting DDR2 enhances tumor response to anti–PD-1 immunotherapy

2019· article· en· W2915186433 on OpenAlexaff
Megan M. Tu, Francis Y. F. Lee, Robert T. Jones, Abigail K. Kimball, Elizabeth Saravia, Robert F. Graziano, Brianne M. Coleman, Krista Menard, Jun Yan, Erin Michaud, Han Chang, Hany Abdel-Hafiz, Andrii I. Rozhok, Jason E. Duex, Neeraj Agarwal, Ana Chauca-Diaz, Linda K. Johnson, Terry L. Ng, John C. Cambier, Eric T. Clambey, James C. Costello, Alan J. Korman, Dan Theodorescu

Bibliographic record

VenueScience Advances · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsOttawa Hospital
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Cancer Institute
KeywordsImmunotherapyIn vivoCancer researchImmune systemMedicineComputational biologyBiologyImmunologyGenetics

Abstract

fetched live from OpenAlex

T cell populations in tumors with DDR2 depletion and those treated with dasatinib when either was combined with anti-PD-1 treatment. Our work provides strong scientific rationale for targeting DDR2 in combination with PD-1 inhibitors.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.308
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 designBench or experimental
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

Citations127
Published2019
Admission routes1
Has abstractyes

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