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Record W2946444383 · doi:10.5489/cuaj.6048

Is nephrectomy no longer beneficial in the management of metastatic kidney cancer?

2019· editorial· en· W2946444383 on OpenAlexaffvenueabout
Anil Kapoor

Bibliographic record

VenueCanadian Urological Association Journal · 2019
Typeeditorial
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNephrectomyMedicineKidney cancerCancerKidneyGeneral surgeryUrologyOncologyInternal medicine

Abstract

fetched live from OpenAlex

T he headlines are clear: "No patient who presents with metastatic kidney cancer benefits from surgical removal of their primary kidney tumour."This dramatic statement was the conclusion of the CARMENA trial findings presented at the American Society of Clinical Oncology (ASCO) meeting in June 2018, stating that cytoreductive nephrectomy should no longer be part of the standard of care for patients with metastatic renal cell carcinoma (mRCC).This trial randomized patients with mRCC with intermediate-and poor-risk criteria to one of two arms -sunitinib alone or cytoreductive nephrectomy followed by sunitinib.In the final analysis, the median overall survival (OS) was longer in the sunitinib alone arm.As a result of this trial conclusion, which spread quickly throughout the kidney cancer community, the rates of cytoreductive nephrectomy have plummeted across Canada.But are these conclusions absolute?Are there any patients with mRCC who would benefit from surgery on their kidney tumour?In this issue of the CUAJ, Mason et al provide guidance to answer these questions through the The Kidney Cancer Network of Canada (KCRNC) consensus statement on the role of cytoreductive nephrectomy for patients with mRCC.The KCRNC, founded by Dr. Michael Jewett from the University of Toronto, is a network of researchers committed to the facilitation of kidney cancer research in Canada, and includes clinical experts, researchers, and patients.The KCRNC works closely with Kidney Cancer Canada to promote and fund kidney cancer research across Canada, and produces periodic consensus statements to guide clinicians in the management of their kidney cancer patients.The role of cytoreductive nephrectomy has become controversial in the management of mRCC; however, it is clear that selected patients with mRCC would certainly benefit from nephrectomy; this KCRNC consensus provides that guidance.Not all patients should be painted with the same brush that nephrectomy is not indicated.Patients that may benefit from cytoreductive nephrectomy include those patients with good performance status, young age, no systemic symptoms, relatively limited burden of disease, favourable-risk status, and select intermediate-risk patients.Patients with poor-risk status probably would not benefit from cytoreductive nephrectomy.An important caveat in this controversy is that the current systemic management has evolved quickly such that tyrosine kinase inhibitors (TKIs), like sunitinib, are no longer the standard treatment for intermediate-and poor-risk patients; the immuno-oncology (IO) class of therapies is the new standard, either in combination with another IO drug or a TKI.(Stay tuned for an upcoming updated KCRNC consensus on these new therapies.)So is this new CARMENA study already irrelevant?The KCRNC recommends that such patients be discussed in multidisciplinary clinics (urologist, medical oncologist, radiation oncologist, nursing, radiologist, and pathologist) if possible to optimize care for your kidney cancer patient.

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)0.011
metaresearch head score (Gemma)0.063
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0040.004
Open science0.0030.001
Research integrity0.0100.016
Insufficient payload (model declined to judge)0.0050.002

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 head0.264
Teacher spread0.247 · 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
GenreEditorial

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

Citations0
Published2019
Admission routes3
Has abstractyes

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