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Record W2957874883 · doi:10.1097/mou.0000000000000657

The evolving role of cytoreductive nephrectomy in metastatic renal cell carcinoma

2019· review· en· W2957874883 on OpenAlexaff
Jeffrey Graham, Bimal Bhindi, Daniel Y.C. Heng

Bibliographic record

VenueCurrent Opinion in Urology · 2019
Typereview
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of CalgaryUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsMedicineSunitinibRenal cell carcinomaNephrectomyRandomized controlled trialObservational studySystemic therapyInternal medicineOncologyUrologyCancerKidney

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To review the evidence related to cytoreductive nephrectomy in metastatic renal cell carcinoma (mRCC) treated in the targeted therapy era, with a focus on observational studies and randomized trials. RECENT FINDINGS: A number of retrospective observational studies exploring the role of cytoreductive nephrectomy have been reported. These have suggested an association between cytoreductive nephrectomy and survival, with hazard ratio estimates ranging from 0.39 to 0.68 in favour of cytoreductive nephrectomy. In contrast, the CARMENA randomized trial demonstrated that sunitinib alone was noninferior to cytoreductive nephrectomy followed by sunitinib in intermediate-risk and poor-risk patients. The results of the SURTIME trial suggest that initial sunitinib followed by a deferred cytoreductive nephrectomy may also be a reasonable approach in select patients. SUMMARY: On the basis of the evidence to date, there is still a role for cytoreductive nephrectomy in the multimodality treatment of mRCC. Careful patient selection is of paramount importance and discussion in multidisciplinary tumour boards is encouraged. As the treatment landscape of mRCC continues to change, the role of cytoreductive nephrectomy in the modern immuno-oncology era will need to be explored.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.093
GPT teacher head0.371
Teacher spread0.278 · 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 teacher head, not a consensus.

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

Citations14
Published2019
Admission routes1
Has abstractyes

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