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Record W2415587476 · doi:10.1136/bmjopen-2015-010981

How do women trade-off benefits and risks in chemotherapy treatment decisions based on gene expression profiling for early-stage breast cancer? A discrete choice experiment

2016· article· en· W2415587476 on OpenAlexafffundabout
Deborah A. Marshall, Ken Deal, Yvonne Bombard, Natasha B. Leighl, Karen V. MacDonald, Maureen Trudeau

Bibliographic record

VenueBMJ Open · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentrePrincess Margaret Cancer CentreUniversity of TorontoMcMaster UniversitySt. Michael's HospitalUniversity of Calgary
FundersCanadian Cancer Society Research InstituteCanadian Centre for Applied Research in Cancer ControlCancer Care OntarioOntario Institute for Cancer ResearchGenomic Health
KeywordsMedicineBreast cancerOncologyInternal medicineFamily medicineCancer

Abstract

fetched live from OpenAlex

OBJECTIVES: Gene expression profiling (GEP) of tumours informs baseline risk prediction, potentially affecting adjuvant chemotherapy decisions for women with early-stage breast cancer. Since only 15% will experience a recurrence, concerns have been raised about potential harms from overtreatment and high GEP costs in publicly funded healthcare systems. We aimed to estimate preferences and personal utility of GEP testing information and benefit-risk trade-offs in chemotherapy treatment decisions. DESIGN, SETTING AND INTERVENTION: Based on literature review and findings from our qualitative research (focus groups, interviews with patients with breast cancer and medical oncologists), we developed a discrete choice experiment (DCE) survey and administered it via an internet panel. The DCE included 12 choice tasks with 5 attributes and 3 alternatives considering orthogonality, D-efficiency and level balance. PARTICIPANTS: The DCE survey was administered to 1004 Canadian women from the general population. MAIN OUTCOME MEASURES: Preferences were analysed using conditional logit and hierarchical Bayes and evaluated for goodness of fit. We conducted simulation analyses for alternative scenarios. RESULTS: GEP test score indicating likely benefit from chemotherapy was the most important attribute. Doctor's clinical estimate of the risk of cancer returning, trust in your cancer doctor and side effects of chemotherapy (temporary and permanent) were relatively less important but showed significant differences among levels. In the scenario analyses, 78% were likely to choose chemotherapy in a high-risk scenario, 55% in a moderate-risk scenario and 33% in a low-risk scenario, with the other attributes held constant. A high GEP score was more important in influencing the choice of chemotherapy for those at intermediate clinical risk. CONCLUSIONS: GEP testing information influences chemotherapy treatment decisions in early-stage breast cancer and varies depending on clinical risk. Clinicians should be aware of these differences and tailor the use of GEP testing accordingly.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.067
GPT teacher head0.381
Teacher spread0.315 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations39
Published2016
Admission routes3
Has abstractyes

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