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Record W2712978371 · doi:10.1001/jama.2017.7105

Effect of First-Line Chemotherapy Combined With Cetuximab or Bevacizumab on Overall Survival in Patients With <i>KRAS</i> Wild-Type Advanced or Metastatic Colorectal Cancer

2017· article· en· W2712978371 on OpenAlexaffabout
Alan P. Venook, Donna Niedzwiecki, Heinz‐Josef Lenz, Federico Innocenti, Briant Fruth, Jeffrey A. Meyerhardt, Deborah Schrag, Claire Greene, Bert H. O’Neil, James N. Atkins, Scott Berry, Blasé N. Polite, Eileen M. O’Reilly, Richard M. Goldberg, Howard S. Höchster, Richard L. Schilsky, Monica M. Bertagnolli, Anthony B. El-Khoueiry, Peter J. Watson, Al B. Benson, Daniel Mulkerin, Robert J. Mayer, Charles D. Blanke

Bibliographic record

VenueJAMA · 2017
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Center for Advancing Translational SciencesNational Cancer Institute
KeywordsMedicineBevacizumabCetuximabFOLFIRIIrinotecanRegimenKRASInternal medicineOncologyOxaliplatinColorectal cancerChemotherapy regimenChemotherapyCancer

Abstract

fetched live from OpenAlex

Importance: Combining biologic monoclonal antibodies with chemotherapeutic cytotoxic drugs provides clinical benefit to patients with advanced or metastatic colorectal cancer, but the optimal choice of the initial biologic therapy in previously untreated patients is unknown. Objective: To determine if the addition of cetuximab vs bevacizumab to the combination of leucovorin, fluorouracil, and oxaliplatin (mFOLFOX6) regimen or the combination of leucovorin, fluorouracil, and irinotecan (FOLFIRI) regimen is superior as first-line therapy in advanced or metastatic KRAS wild-type (wt) colorectal cancer. Design, Setting, and Participants: Patients (≥18 years) enrolled at community and academic centers throughout the National Clinical Trials Network in the United States and Canada (November 2005-March 2012) with previously untreated advanced or metastatic colorectal cancer whose tumors were KRAS wt chose to take either the mFOLFOX6 regimen or the FOLFIRI regimen as chemotherapy and were randomized to receive either cetuximab (n = 578) or bevacizumab (n = 559). The last date of follow-up was December 15, 2015. Interventions: Cetuximab vs bevacizumab combined with either mFOLFOX6 or FOLFIRI chemotherapy regimen chosen by the treating physician and patient. Main Outcomes and Measures: The primary end point was overall survival. Secondary objectives included progression-free survival and overall response rate, site-reported confirmed or unconfirmed complete or partial response. Results: Among 1137 patients (median age, 59 years; 440 [39%] women), 1074 (94%) of patients met eligibility criteria. As of December 15, 2015, median follow-up for 263 surviving patients was 47.4 months (range, 0-110.7 months), and 82% of patients (938 of 1137) experienced disease progression. The median overall survival was 30.0 months in the cetuximab-chemotherapy group and 29.0 months in the bevacizumab-chemotherapy group with a stratified hazard ratio (HR) of 0.88 (95% CI, 0.77-1.01; P = .08). The median progression-free survival was 10.5 months in the cetuximab-chemotherapy group and 10.6 months in the bevacizumab-chemotherapy group with a stratified HR of 0.95 (95% CI, 0.84-1.08; P = .45). Response rates were not significantly different, 59.6% vs 55.2% for cetuximab and bevacizumab, respectively (difference, 4.4%, 95% CI, 1.0%-9.0%, P = .13). Conclusions and Relevance: Among patients with KRAS wt untreated advanced or metastatic colorectal cancer, there was no significant difference in overall survival between the addition of cetuximab vs bevacizumab to chemotherapy as initial biologic treatment. Trial Registration: clinicaltrials.gov identifier: NCT00265850.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.297
Teacher spread0.284 · 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 designRandomized trial
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

Citations893
Published2017
Admission routes2
Has abstractyes

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