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Record W4417183496 · doi:10.1038/s41581-025-01031-3

The nephrotoxic effects of anti-cancer therapies: consensus report of the 34th Acute Disease Quality Initiative workgroup

2025· article· en· W4417183496 on OpenAlex
Amanda DeMauro Renaghan, Marlies Ostermann, Claudio Ronco, Karen K. Ballen, Laura Cosmai, Roberta Fenoglio, Matteo Floris, Lui G. Forni, Douglas E. Gladstone, Ilya Glezerman, Stuart L. Goldstein, Shruti Gupta, Sandra M. Herrmann, Edgar A. Jaimes, Kenar D. Jhaveri, Sabine Karam, Abhijat Kitchlu, Heather Landau, Sheron Latcha, David E. Leaf, Paolo Lentini, Jolanta Małyszko, Glen S. Markowitz, Naoka Murakami, Antonello Pani, Mark A. Perazella, Arash Rashidi, Dario Roccatello, Elad Sharon, Ben Sprangers, Vladimı́r Tesař, Verônica T. Costa e Silva, Rimda Wanchoo, Andrew Whitman, Biruh Workeneh, Diana Zepeda‐Orozco, Mitchell H. Rosner

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 Reviews Nephrology · 2025
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced organ toxicity mitigation
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNational Cancer InstituteNational Institutes of HealthCytoSorbents EuropeAkebia TherapeuticsNational Institute for Health and Care ResearchBioPortoProthenaAlexion PharmaceuticalsGlaxoSmithKlineNational Institute of Diabetes and Digestive and Kidney DiseasesAmgenPfizerSphingotec GmbHMersana TherapeuticsScience and Engineering Research BoardFresenius Medical Care North AmericaNiproAmerican Society of NephrologyAstraZenecaEli Lilly and Company
KeywordsWorkgroupNephrotoxicityKidney diseaseAcute kidney injuryDosingDiseaseChimeric antigen receptorRenal replacement therapyCancer

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)0.001
metaresearch head score (Gemma)0.002
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 score0.531
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.359
Teacher spread0.342 · 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