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Record W2113122060 · doi:10.1056/nejmoa1103799

A Phase 3 Trial of Bevacizumab in Ovarian Cancer

2011· article· en· W2113122060 on OpenAlexaff
Timothy Perren, Ann Marie Swart, Jacobus Pfisterer, Jonathan A. Ledermann, Éric Pujade-Lauraine, Gunnar B. Kristensen, Mark S. Carey, Philip Beale, Andrés Cervantes, Christian Kurzeder, Andreas du Bois, Jalid Sehouli, Rainer Kimmig, Anne Stähle, Fiona Collinson, Sharadah Essapen, Charlie Gourley, Alain Lortholary, Frédèric Selle, Mansoor R. Mirza, Arto Leminen, Marie Plante, Dan Stark, Wendi Qian, Mahesh Parmar, Amit M. Oza

Bibliographic record

VenueNew England Journal of Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsUniversité LavalUniversity of British Columbia
Fundersnot available
KeywordsMedicineBevacizumabHazard ratioOvarian cancerInternal medicineCarboplatinRegimenOncologyProgression-free survivalInterim analysisCancerConfidence intervalSurgeryRandomized controlled trialChemotherapyCisplatin

Abstract

fetched live from OpenAlex

BACKGROUND: Angiogenesis plays a role in the biology of ovarian cancer. We examined the effect of bevacizumab, the vascular endothelial growth factor inhibitor, on survival in women with this disease. METHODS: We randomly assigned women with ovarian cancer to carboplatin (area under the curve, 5 or 6) and paclitaxel (175 mg per square meter of body-surface area), given every 3 weeks for 6 cycles, or to this regimen plus bevacizumab (7.5 mg per kilogram of body weight), given concurrently every 3 weeks for 5 or 6 cycles and continued for 12 additional cycles or until progression of disease. Outcome measures included progression-free survival, first analyzed per protocol and then updated, and interim overall survival. RESULTS: A total of 1528 women from 11 countries were randomly assigned to one of the two treatment regimens. Their median age was 57 years; 90% had epithelial ovarian cancer, 69% had a serous histologic type, 9% had high-risk early-stage disease, 30% were at high risk for progression, and 70% had stage IIIC or IV ovarian cancer. Progression-free survival (restricted mean) at 36 months was 20.3 months with standard therapy, as compared with 21.8 months with standard therapy plus bevacizumab (hazard ratio for progression or death with bevacizumab added, 0.81; 95% confidence interval, 0.70 to 0.94; P=0.004 by the log-rank test). Nonproportional hazards were detected (i.e., the treatment effect was not consistent over time on the hazard function scale) (P<0.001), with a maximum effect at 12 months, coinciding with the end of planned bevacizumab treatment and diminishing by 24 months. Bevacizumab was associated with more toxic effects (most often hypertension of grade 2 or higher) (18%, vs. 2% with chemotherapy alone). In the updated analyses, progression-free survival (restricted mean) at 42 months was 22.4 months without bevacizumab versus 24.1 months with bevacizumab (P=0.04 by log-rank test); in patients at high risk for progression, the benefit was greater with bevacizumab than without it, with progression-free survival (restricted mean) at 42 months of 14.5 months with standard therapy alone and 18.1 months with bevacizumab added, with respective median overall survival of 28.8 and 36.6 months. CONCLUSIONS: Bevacizumab improved progression-free survival in women with ovarian cancer. The benefits with respect to both progression-free and overall survival were greater among those at high risk for disease progression. (Funded by Roche and others; ICON7 Controlled-Trials.com number, ISRCTN91273375.).

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)0.002
metaresearch head score (Gemma)0.002
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.066
GPT teacher head0.351
Teacher spread0.285 · 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

Citations2,152
Published2011
Admission routes1
Has abstractyes

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