MétaCan
Menu
← Back to cohort
Record W2400789541 · doi:10.1158/1557-3265.ovca15-b21

Abstract B21: <i>BRCA</i> mutation status is not associated with better long-term survival from epithelial ovarian cancer.

2016· article· en· W2400789541 on OpenAlexaffabout
Joanne Kotsopoulos, Isabel Fan, Barry P. Rosen, Taymaa May, Harvey A. Risch, Ping Sun, Steven A. Narod

Bibliographic record

VenueClinical Cancer Research · 2016
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsPrincess Margaret Cancer CentreLunenfeld-Tanenbaum Research InstituteWomen's College Hospital
Fundersnot available
KeywordsOvarian cancerMedicineCancerBRCA mutationOncologyCancer registryPopulationMutationInternal medicineDiseaseCohortMedical recordGynecologyGeneticsBiologyGene

Abstract

fetched live from OpenAlex

Abstract Background: Previous studies have described better survival for women with epithelial ovarian cancer and an inherited BRCA1 or BRCA2 mutation compared to those without a mutation. Most of these studies had short follow-up periods. It is of importance to clarify the disease-specific mortality outcomes of BRCA mutation carriers using data from an unselected population with long follow-up. The overall goal of the current study was to evaluate the long-term mortality outcomes based on BRCA mutation status among women diagnosed with epithelial ovarian cancer. Methods: Women with unselected epithelial ovarian cancer diagnosed in Ontario, Canada from 1995-1999 and 2002-2004 included in the current study. Tumor histology was based on review of pathology records while clinical and treatment information was obtained from individual medical records. Survival status was determined by linkage to the Ontario Cancer Registry which provides information on date and cause of death. We also estimated the annual mortality for members of the cohort based on a life-table approach for each one-year period after the date of diagnosis until 12 years after diagnosis. Results: Of the 1,421 women with a diagnosis of invasive epithelial ovarian cancer, 109 (7.7%) had a BRCA1 mutation and 68 (4.8%) had a BRCA2 mutation while 1,244 (87.5%) were non-carriers. The mean age at diagnosis (in years) was 57.7 for non-carriers, 50.9 for BRCA1 mutation carriers and 57.4 for BRCA2 mutation carriers. Seventy-three percent of BRCA1 mutation carriers, 78% of BRCA2 mutation carriers and 52% of non-carriers were diagnosed with a serous tumor. The two-year survival rates were 78% for non-carriers, 85% for BRCA1 mutation carriers and 100% for BRCA2 mutation carriers. The five-year survival rates were 56% for non-carriers, 52% for BRCA1 mutation carriers and 61% for BRCA2 mutation carriers. The 12-year survival rates were 44% for non-carriers, 32% for BRCA1 mutation carriers and 28% for BRCA2 mutation carriers. At 12-years, the survival experience of BRCA mutation carriers was significantly worse than non-carriers (OR = 1.81; P < 0.0001). Among the women who died, the mean duration from diagnosis to death was 4.47 years for non-carriers, 4.78 years for BRCA1 mutation carriers and 5.13 years for BRCA2 mutation carriers. Conclusion: Findings from this study suggest a short-term survival advantage among women with a BRCA mutation but no impact on overall, long-term survival. This observation may reflect a better acute response of BRCA carriers to chemotherapy that is eventually diminished over time. Citation Format: Joanne Kotsopoulos, Isabel Fan, John McLaughlin, Barry Rosen, Taymaa May, Harvey Risch, Ping Sun, Steven Narod. BRCA mutation status is not associated with better long-term survival from epithelial ovarian cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr B21.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.444

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.190
GPT teacher head0.479
Teacher spread0.289 · 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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueClinical Cancer Research→Same topicOvarian cancer diagnosis and treatment→French-language works237,207→