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Record W2052762622 · doi:10.1097/coc.0b013e3181574084

Sorafenib for Metastatic Renal Cancer

2008· article· en· W2052762622 on OpenAlexaff
Rachel P. Riechelmann, Soo Chin, Lisa Wang, Ian F. Tannock, Malcolm J. Moore, Jennifer J. Knox

Bibliographic record

VenueAmerican Journal of Clinical Oncology · 2008
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineSorafenibRashInternal medicineAdverse effectPopulationPlaceboCommon Terminology Criteria for Adverse EventsOncologySurgeryCancerCohortGastroenterologyHepatocellular carcinomaPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: Sorafenib, an oral multikinase inhibitor, prolonged progression-free survival when compared with placebo, as second-line therapy for patients with metastatic renal carcinoma (MRC). Grade 3/4 adverse events were reported in 12% of patients. This study presents sorafenib's efficacy and safety in a less selected cohort of patients enrolled in an expanded access program. METHODS: Patients with MRC received sorafenib 400 mg twice daily until disease progression. Tumor response was evaluated by RECIST criteria. Adverse events were graded by NCI common toxicity criteria. RESULTS: From November 2005 to August 2006, 58 patients were enrolled. The median progression-free survival was 7.5 months (95% CI: 5.4-11.3), and the best responses among 54 patients were 11 (20%) confirmed partial responses, 15 (28%) stable diseases for > or =6 months; 10 patients (18%) had early progression at 8 weeks. Grade 3/4 adverse events occurred in 37 patients (64%; 95% CI: 50%-76%), the most frequent being skin rash in 17 patients (29%), and hand-foot syndrome in 9 patients (15%). Thirty-six (62%) patients required dose reductions and/or treatment interruptions. CONCLUSIONS: Sorafenib is effective in a less selected patient population with MRC but leads to more toxicity than described previously.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.228
GPT teacher head0.504
Teacher spread0.276 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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".

Quick stats

Citations50
Published2008
Admission routes1
Has abstractyes

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