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Abstract P6-08-08: Age-Related differences in clinicopathologic features and survival amongst women with triple negative breast cancer: A population-based study

2018· article· en· W2790544699 on OpenAlexaff
SM Wong, J-F Boileau, Karyne Martel, Cristiano Ferrario, Mark Basik

Bibliographic record

VenueCancer Research · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsJewish General HospitalMcGill University Health Centre
Fundersnot available
KeywordsMedicineTriple-negative breast cancerBreast cancerCohortPopulationEpidemiologySurveillance, Epidemiology, and End ResultsInternal medicineOncologyLymph nodeCancerProportional hazards modelCancer registry

Abstract

fetched live from OpenAlex

Abstract INTRODUCTION: Women with triple-negative breast cancers (TNBC) have a tendency to present at younger ages and with more advanced disease. We sought to comprehensively evaluate the characteristic features, surgical management, and survival outcomes of a large, population-based cohort of patients with TNBC according to age at diagnosis. METHODS: We queried the Surveillance, Epidemiology, and End Results (SEER) database to identify women aged 18 years or older with a diagnosis of TNBC between 2010-2014. Clinicopathologic and treatment level variables were compared amongst TNBC patients according to age at the time of TNBC diagnosis. The Kaplan-Meier method and Cox PH Regression was then used to examine short-term breast cancer-specific survival (BCSS) outcomes. RESULTS: Between 2010-2014, 214,138 women were diagnosed with breast cancer, of which 23,614 (11.13%) had TNBC. The median age at TNBC diagnosis was 57.7 years. Younger TNBC patients were more likely to be of African American (<40 years, 20.1% vs. ≥70 years, 15.5%; p<0.001) or Hispanic race (<40 years, 21.9% vs. ≥70 years, 7.0%; p<0.001), diagnosed with larger tumors (T2-T3; <40 years, 70.2%; 40-49 years, 61.7%; 50-59 years, 55%; 60-69 years, 48.1%; ≥70 years, 49.5%; p<0.001) and present with lymph node positive disease (<40 years, 36.7%; 40-49 years, 34.8%; 50-59 years, 32.5%; 60-69 years, 27.5%; ≥70 years, 27.9%; p<0.001). With respect to local therapy, younger women also had a greater tendency to undergo bilateral mastectomy (<40 years, 34.3%; 40-49 years, 23.1%; 50-59 years, 13.4%; 60-69 years, 8.4%; ≥70 years, 3.3%; p<0.001). The estimated one and four-year BCSS for the entire cohort was 94.4% and 79.7%, respectively, with the youngest women <40 years and older women ≥70 years demonstrating the poorest unadjusted BCSS at four years (<40 years, 76.95%; 40-49 years, 82.1%; 50-59 years 80.9%; 60-69 years 81.7%; ≥70 years, 78.6%; log rank p<0.001). In Cox PH analysis adjusting for race, stage, pathologic features, and local therapy, age greater than 70 years remained significantly associated with worse cancer-specific survival (HR1.60, 95% CI 1.39-1.84). CONCLUSION: In the population studied, more than 40% of very young women with TNBC are of African American or Hispanic race. When compared to older ages, younger women with TNBC are more likely to receive bilateral mastectomy and have more advanced stage at presentation. Women at both age extremes (≥70 years and <40 years at diagnosis) demonstrate worse cancer-specific survival outcomes. In older women, this may be due to undertreatment, and in younger women, to delays in diagnosis and/or worse tumor biology. Further studies are needed to evaluate age-related discrepancies in local and systemic therapy and cancer-specific survival in TNBC. Citation Format: Wong SM, Boileau J-F, Martel K, Ferrario C, Basik M. Age-Related differences in clinicopathologic features and survival amongst women with triple negative breast cancer: A population-based study [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P6-08-08.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.369
Teacher spread0.327 · 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
Published2018
Admission routes1
Has abstractyes

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