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Abstract P3-04-11: Systemic treatment decision making for patients with stage I and II, hormone receptor positive, her2/neu negative breast cancer

2012· article· en· W2312649718 on OpenAlexaffabout
Xiaowen Zhu, N. Graham, Lise Paquet, S Dent, Xin Song

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsCarleton UniversityOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineInternal medicineBreast cancerStage (stratigraphy)Adjuvant chemotherapyOncologyDemographicsGynecologyHER2 negativeCancerMetastatic breast cancer

Abstract

fetched live from OpenAlex

Abstract Background: Oncotype DX is a clinically validated risk stratification tool that can predict the risk of recurrence and the benefit of adjuvant chemotherapy in women with hormone receptor positive (HR+), HER2/neu negative early stage breast cancer (EBC). This tool has been available to oncologists in Ontario since April 2010 at significant cost, yet no guidelines exist regarding their use. This retrospective chart review examined the factors that were associated with use of Oncotype DX at a tertiary care cancer centre. Materials and methods: One hundred patients (pts) diagnosed with HR+, HER2/neu negative EBC (stage I-II), who underwent Oncotype DX testing between April 1, 2010, and June 30, 2011 were included in the study. A second control group of 100 patients with similar disease characteristics but who did not receive Oncotype DX testing were randomly selected. Data collection included demographics, tumor grade and stage, and Adjuvant! Online recurrence risk scores. The distribution of patients in each category was compared using the chi-square test to detect statistically significant differences between distributions. Results: Median age in the Oncotype DX group was 58 years (r: 26–77) and 63 years (r: 30–81) in the control group. 20 patients in the Oncotype DX group were aged 35–49, 57 patients were aged 50–64, and 23 patients were aged 65 or older, while the control group had 16, 43, and 41 patients, respectively (p = 0.02). The Oncotype DX group had 72 pre- and perimenopausal pts and 28 postmenopausal patients, while the control group had 81 and 19 patients, respectively (p = 0.13). 20, 56, and 24 pts in the Oncotype DX group had grade 1, 2, and 3 histology, respectively, vs. 44, 44, and 12, respectively in the control group (p < 0.01). The Oncotype DX group had 7 patients with tumors between 1–10 mm, 55 between 10.1–20 mm, 34 between 20.1–50 mm, and 4 greater than 50 mm, vs. 29, 42, 23, and 1, respectively in the control group (p < 0.01). When 10-year Adjuvant Online recurrence scores were calculated using tamoxifen, 17, 67, and 16 patients in the Oncotype DX group had risk scores of <15, 15–25, and >25, respectively, vs. 62, 33, and 5 in the control group (p < 0.01). When the scores were calculated using tamoxifen plus an aromatase inhibitor, 49, 42, and 9 patients in the Oncotype DX group, and 75, 24, and 1 patients in the control group fell into these categories, respectively (p < 0.01). Median Oncotype DX recurrence score was 17 (r: 0–70), with 10-year recurrence risk of 11% (r:3–34%). Conclusions: This single-centre series is aimed at identifying potential clinical and pathological factors which can influence physicians' decision to request Oncotype DX testing for pts with EBC. Physicians were more likely to request Oncotype DX testing for patients that were younger, had larger and higher grade tumors, and higher Adjuvant! Online recurrence risk scores. These results will be used to design a prospective study evaluating these factors and how Oncotype DX testing may influence treatment decision making. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P3-04-11.

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)0.002
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0050.000

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 head0.027
GPT teacher head0.358
Teacher spread0.330 · 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
Published2012
Admission routes2
Has abstractyes

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