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Record W2133504690 · doi:10.1586/erp.13.4

Is the 21-gene recurrence score a cost-effective assay in endocrine-sensitive node-negative breast cancer?

2013· review· en· W2133504690 on OpenAlexaff
Nathan Lamond, Chris Skedgel, Tallal Younis

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsDalhousie UniversityQueen Elizabeth II Health Sciences Centre
Fundersnot available
KeywordsMedicineBreast cancerOncologyContext (archaeology)Internal medicineChemotherapyCancerEndocrine systemGynecologyHormoneBiology

Abstract

fetched live from OpenAlex

The 21-gene recurrence score (RS) is a gene expression profile assay currently endorsed for use in patients with endocrine-sensitive node-negative breast cancers. The RS has been shown to augment current 'prognostic' and 'predictive' assessments of relapse risk and chemotherapy benefits, respectively, and lead to significant change in oncologists' recommendations for adjuvant chemotherapy, with an overall reduction in chemotherapy utilization. The RS (Oncotype DX) is marketed by Genomic Health Inc. (CA, USA) and currently retails for approximately US$4290 per patient. Like all novel tests/therapies, however, these upfront costs should be examined in the context of all its clinical benefits through cost-effectiveness or cost-utility evaluations. This review highlights the clinical evidence supporting RS testing for patients with endocrine-sensitive node-negative breast cancers, and examines all published economic evaluations that examined its 'value for money' in this setting.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.068
GPT teacher head0.521
Teacher spread0.453 · 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 designSystematic review
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

Citations14
Published2013
Admission routes1
Has abstractyes

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