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Record W4399428966 · doi:10.2215/cjn.0000000000000508

Assessment of GFR in Patients with Cancer

2024· review· en· W4399428966 on OpenAlexaff
Abhijat Kitchlu, Verônica T. Costa e Silva, Shuchi Anand, Jaya Kala, Ala Abudayyeh, Lesley A. Inker, Mitchell H. Rosner, Sabine Karam, Prakash Gudsoorkar, Shruti Gupta, Sheldon Chen, Nattawat Klomjit, Nelson Leung, Tomaž Milanez, Shveta S. Motwani, Sheikh B. Khalid, Vinay Srinivasan, Rimda Wanchoo, Jan H. Beumer, Geoffrey Liu, Nizar M. Tannir, Ani Orchanian‐Cheff, Yimin Geng, Sandra M. Herrmann

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2024
Typereview
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsPrincess Margaret Cancer CentrePublic Health OntarioUniversity of TorontoUniversity Health Network
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer Institute
KeywordsMedicineCreatinineRenal functionInternal medicinePopulationDosingCancerNephrologyUrologyCystatin CKidney diseaseOncologyIntensive care medicineEndocrinology

Abstract

fetched live from OpenAlex

Accurate assessment of GFR is crucial to guiding drug eligibility, dosing of systemic therapy, and minimizing the risks of both undertreatment and toxicity in patients with cancer. Up to 32% of patients with cancer have baseline CKD, and both malignancy and treatment may cause kidney injury and subsequent CKD. To date, there has been lack of guidance to standardize approaches to GFR estimation in the cancer population. In this two-part statement from the American Society of Onco-Nephrology, we present key messages for estimation of GFR in patients with cancer, including the choice of GFR estimating equation, use of race and body surface area adjustment, and anticancer drug dose-adjustment in the setting of CKD. These key messages are based on a systematic review of studies assessing GFR estimating equations using serum creatinine and cystatin C in patients with cancer, against a measured GFR comparator. The preponderance of current data involving validated GFR estimating equations involves the CKD Epidemiology Collaboration (CKD-EPI) equations, with 2508 patients in whom CKD-EPI using serum creatinine and cystatin C was assessed (eight studies) and 15,349 in whom CKD-EPI with serum creatinine was assessed (22 studies). The former may have improved performance metrics and be less susceptible to shortfalls of eGFR using serum creatinine alone. Since included studies were moderate quality or lower, the American Society of Onco-Nephrology Position Committee rated the certainty of evidence as low. Additional studies are needed to assess the accuracy of other validated eGFR equations in patients with cancer. Given the importance of accurate and timely eGFR assessment, we advocate for the use of validated GFR estimating equations incorporating both serum creatinine and cystatin C in patients with cancer. Measurement of GFR via exogenous filtration markers should be considered in patients with cancer for whom eGFR results in borderline eligibility for therapies or clinical trials.

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 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)0.006
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.052
GPT teacher head0.454
Teacher spread0.402 · 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 designSystematic review
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

Citations42
Published2024
Admission routes1
Has abstractyes

Explore more

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