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First-line sunitinib versus pazopanib in metastatic renal cell carcinoma (mRCC): Results from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).

2016· article· en· W2590029130 on OpenAlexaff
José Manuel Ruiz Morales, J. Connor Wells, Frede Donskov, Georg A. Bjarnason, Jae‐Lyun Lee, Jennifer J. Knox, Benoit Beuselinck, Ulka N. Vaishampayan, James Brugarolas, Reuben Broom, Aristotelis Bamias, Takeshi Yuasa, Sandhya Srinivas, D. Scott Ernst, Carmel Pezaro, Lori Wood, Christian Kollmannsberger, Brian I. Rini, Toni K. Choueiri, Daniel Yick Chin Heng

Bibliographic record

VenueJournal of Clinical Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsQueen Elizabeth II Health Sciences CentreOttawa Regional Cancer FoundationBC Cancer AgencyPrincess Margaret Cancer CentreUniversity of TorontoSunnybrook Health Science CentreUniversity of Calgary
Fundersnot available
KeywordsMedicinePazopanibSunitinibRenal cell carcinomaHazard ratioInternal medicineOncologyPopulationUrologyOverall survivalGastroenterologyConfidence interval

Abstract

fetched live from OpenAlex

544 Background: Sunitinib (SU) and Pazopanib (PZ) have been compared head-to-head in the first-line phase III COMPARZ study in metastatic renal cell carcinoma (mRCC). We compared SU versus PZ, to confirm outcomes and subsequent second-line therapy efficacy in a population-based setting. Methods: We used the IMDC to assess overall survival (OS), progression-free survival (PFS), response rate (RR) and performed proportional hazard regression adjusting for IMDC prognostic groups. Second-line OS2 and PFS2 were also evaluated. Results: We obtained data from 3,606 patients with mRCC treated with either first line SU (n=3226) or PZ (n=380) with an overall median follow-up of 43.5 months (m) (CI95% 41.4 – 46.4). IMDC risk group distribution for favorable prognosis was 440 (17.3%) for SU vs 72 (25%) for PZ, intermediate prognosis 1414 (55.6%) for SU vs 153 (53%) for PZ, poor prognosis 689 (27.1%) for SU vs 62 (22%) for PZ, p= 0.0027. We found no difference between SU vs. PZ for OS (20.1 [CI95% 18.76-21.42] vs. 23.68 m [CI95% 19.54 - 28.81] p=0.19), PFS (7.22 [CI95% 6.76 - 7.78] vs. 6.83 m [CI95% 5.58 - 8.27] p=0.49). The RR was similar in both groups (Table 1). Adjusted HR for OS and PFS were 0.952 (CI95% 0.788 – 1.150 p=0.61) and 1.052 (CI95% 0.908 – 1.220 p = 0.49), respectively. We also found no difference in any second-line treatment between either post-SU vs. post-PZ groups for OS2 (12.88 [CI95% 11.89 – 14.19] vs. 12.91 m [CI95% 10.3 – 19.1] p=0.47) and PFS2 (3.67 [CI95% 3.38 – 3.87] vs. 4.53 m [CI95% 3.08 – 5.35] p=0.4). There was no statistical difference in OS2 and PFS2 if everolimus was used after SU or PZ (p = 0.33 and p = 0.41, respectively) or if axitinib was used after SU or PZ (p = 0.73 and p = 0.72, respectively). Conclusions: We confirmed in real world practice, that SU and PZ have similar efficacy in the first-line setting for mRCC and do not affect outcomes with subsequent second-line treatment. [Table: see text]

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.189
GPT teacher head0.408
Teacher spread0.218 · 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 designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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