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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 OpenAlexaff
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

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.

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)0.084
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.084
Threshold uncertainty score0.444

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0840.071
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.009
Bibliometrics0.0040.005
Science and technology studies0.0010.002
Scholarly communication0.0050.003
Open science0.0060.006
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0020.001

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

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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2025
Admission routes1
Has abstractno

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