MétaCan
Menu
Back to cohort
Record W4221100608 · doi:10.3390/curroncol29030163

The Role of the 21-Gene Recurrence Score® Assay in Hormone Receptor-Positive, Node-Positive Breast Cancer: The Canadian Experience

2022· review· en· W4221100608 on OpenAlexafffundvenueabout
Mariya Yordanova, Saima Hassan

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de MontréalUniversité de Sherbrooke
FundersFonds de Recherche du Québec - Santé
KeywordsMedicineBreast cancerHormone receptorOncologyGeneInternal medicineReceptorBioinformaticsCancerCancer researchGeneticsBiology

Abstract

fetched live from OpenAlex

(RS) Assay. While the utility of the assay was initially demonstrated among node-negative patients, recent studies have also demonstrated the assay's prognostic and predictive value in node-positive patients. In Canada, the RS assay is reimbursed by provincial health insurance plans, but not all provinces have approved the use of the assay for patients with node-positive disease. Here, we provide an overview of the clinical factors that influence physician recommendation of the RS assay and, alternatively, the impact of the RS assay on patient treatment decisions in Canada. We performed a comprehensive review of the impact of the assay upon physician treatment decisions and cost in node-positive breast cancer patients within Canada and other countries. Furthermore, we evaluated biomarkers that can predict the RS result, in addition to other genomic assays that predict recurrence risk among node-positive patients. Overall, the 21-gene RS assay was shown to be a cost-effective tool that significantly reduced the use of chemotherapy in node-positive breast cancer patients in Canada.

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)0.004
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.919
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.355
Teacher spread0.309 · 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
GenreReview

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

Citations11
Published2022
Admission routes4
Has abstractyes

Explore more

Same venueCurrent OncologySame topicBreast Cancer Treatment StudiesFrench-language works237,207