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Record W4293243564 · doi:10.1016/j.kint.2022.07.019

Acute kidney injury in patients receiving pembrolizumab combination therapy versus pembrolizumab monotherapy for advanced lung cancer

2022· article· en· W4293243564 on OpenAlexafffund
Shruti Gupta, Ian A. Strohbehn, Qiyu Wang, Rituvanthikaa Seethapathy, Sandra M. Herrmann, Ala Abudayyeh, Abhinav Malik, Christopher A. Carlos, Wei‐Ting Chang, Pazit Beckerman, Zain Mithani, Chintan V. Shah, Amanda D. Renaghan, Sophie de Seigneux, Luca Campedel, Daniel Sanghoon Shin, Gaia Coppock, Pablo García, Arash Rashidi, Ben Sprangers, Karolina Benesova, Astrid Weins, Yiqin Zuo, Kerry L. Reynolds, David E. Leaf, Meghan E. Sise, Joe‐Elie Salem, Corinne Isnard Bagnis, Harkarandeep Singh, Shveta S. Motwani, Naoka Murakami, Maria Clarissa Tio, Suraj Sarvode Mothi, Umut Selamet, Kai M. Schmidt‐Ott, Rimda Wanchoo, Yuriy Khanin, Jamie S. Hirsch, Vipulbhai Sakhiya, Daniel Stalbow, Sylvia Wu, Marlies Ostermann, Nina Seylanova, Armando Cennamo, Anne Rigg, Nisha Shaunak, Zoé A. Kibbelaar, Priya Deshpande, Harish Seethapathy, Meghan Lee, Ian A. Strohbhen, Busra Isik, Ilya Glezerman, Sunandana Chandra, Sethu M. Madhavan, Dwight H. Owen, Marium Husain, Sharon Mini, Shuchi Anand, Aydin Kaghazchi, Sunil Rangarajan, Grace Cherry, Raymond K. Hsu, Andrey Kisel, Sheru Kansal, Nicole Albert, Katherine Carter, Vicki Donley, Tricia Young, Heather Cigoi, Thibaud Koessler, Els Wauters, Mark Eijgelsheim, Javier Pagán, Jonathan J. Hogan, Omar Mamlouk, Jamie S. Lin, Valda D. Page, Samuel Short, Elizabeth Gaughan, María José Soler, Clara García-Carro, Sheila Bermejo, Enriqueta Felip, Eva Muñoz‐Couselo, María Josep Carreras

Bibliographic record

VenueKidney International · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesSchool of Medicine, Stanford UniversityNational Institute of General Medical SciencesUniversitätsklinikum HeidelbergSorbonne UniversitéFundació Institut de Recerca Hospital Universitari Vall d’HebronGenentechVlaamse regeringOtsuka PharmaceuticalUniversitair Medisch Centrum GroningenUniversity of Texas MD Anderson Cancer CenterUnion Chimique BelgeInternational Society of NephrologyUniversity of WashingtonMemorial Sloan-Kettering Cancer CenterOhio State UniversityUniversity of California, Los AngelesMedacFonds Wetenschappelijk OnderzoekUniversity of PennsylvaniaUniversität HeidelbergGilead SciencesLeonard M. Miller School of MedicineUniversity of TorontoNateraBrigham and Women's HospitalAssistance publique-Hôpitaux de ParisMassachusetts General HospitalGlaxoSmithKlineMayo ClinicUniversity of MiamiNorthwestern UniversityPfizerEli Lilly and CompanyBristol-Myers SquibbAstraZenecaAstex PharmaceuticalsAmerican Society of Nephrology
KeywordsPembrolizumabMedicineLung cancerInternal medicineOncologyKidney cancerAcute kidney injuryCombination therapyCancerUrologyImmunotherapy

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.318
Teacher spread0.305 · 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

Citations13
Published2022
Admission routes2
Has abstractno

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