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Record W2989830802 · doi:10.1016/j.euo.2019.11.001

Outcomes of Patients with Metastatic Renal Cell Carcinoma Treated with Targeted Therapy After Immuno-oncology Checkpoint Inhibitors

2019· article· en· W2989830802 on OpenAlexaff
Jeffrey Graham, Amishi Y. Shah, J. Connor Wells, Rana R. McKay, Ulka N. Vaishampayan, Aaron R. Hansen, Frede Donskov, Georg A. Bjarnason, Benoit Beuselinck, Guillermo de Velasco, Marco Iafolla, Mei Sheng Duh, Lynn Huynh, Rose Chang, Giovanni Zanotti, Krishnan Ramaswamy, Toni K. Choueiri, Nizar M. Tannir, Daniel Y.C. Heng

Bibliographic record

VenueEuropean Urology Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsSunnybrook Health Science CentreCancerCare ManitobaHealth Sciences CentreUniversity of ManitobaPrincess Margaret Cancer CentreUniversity of Calgary
FundersInstituto de Salud Carlos IIIGenentechEisaiAmerican Psychosocial Oncology SocietyExelixisNorth Carolina GlaxoSmithKline FoundationEMD SeronoNovartisMerckF. Hoffmann-La RocheRocheAstellas Pharma USHealth Research Fund of Central Denmark RegionDana-Farber Cancer InstitutePfizerIpsen BiopharmaceuticalsBayerAstraZenecaEli Lilly and CompanyBristol-Myers Squibb
KeywordsMedicineRenal cell carcinomaOncologyInternal medicineNivolumabImmunotherapyCancer

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.230
Teacher spread0.220 · 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

Citations37
Published2019
Admission routes1
Has abstractyes

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