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Record W2075644080 · doi:10.1097/spc.0b013e32832e466b

Cytoreductive nephrectomy in metastatic renal cell carcinoma: the evolving role of surgery in the era of molecular targeted therapy

2009· review· en· W2075644080 on OpenAlexaff
Kevin Kwan, Anil Kapoor

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2009
Typereview
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsJuravinski Cancer CentreMcMaster University
Fundersnot available
KeywordsMedicineRenal cell carcinomaNephrectomyTargeted therapyUrologyKidneyInternal medicineOncologyCancer

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Despite contemporary surgical and therapeutic innovation, renal cell carcinoma remains the most lethal of the urologic malignancies. Up to a third of patients with renal cell carcinoma have metastatic disease at presentation and 30% with localized disease will eventually progress to metastatic disease. In the past, cytoreductive nephrectomy was reserved for palliative circumstances. RECENT FINDINGS: With the emergence and integration of targeted therapies into current treatment protocols, the role of cytoreductive nephrectomy should be reexamined. Three targeted therapy trials revolutionized the management of metastatic renal cell carcinoma by showing unprecedented efficacy and tolerable therapeutic options. SUMMARY: The benefits of targeted therapy observed in clinical trials have been in the setting of prior cytoreductive nephrectomy. Therefore, any evidence for the potential benefit of cytoreductive nephrectomy in the era of targeted therapy is currently extrapolated and it is unclear where surgery integrates into the current treatment ladder. Other surgical approaches such as laparoscopy and nephron-sparing surgery have also been transferred to a carefully selected metastatic disease population. The concept of presurgical systemic therapy, though not new to oncology, is novel for metastatic renal cell carcinoma treated with targeted therapy. With the maturation of ongoing multicenter clinical trials, answers to these questions will hopefully be clarified.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.713
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
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 head0.114
GPT teacher head0.376
Teacher spread0.262 · 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 designObservational
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

Citations7
Published2009
Admission routes1
Has abstractyes

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