Uptake of a 21-Gene Expression Assay in Breast Cancer Practice: Views of Academic and Community-Based Oncologists
Bibliographic record
Abstract
PURPOSE: Advances in personalized medicine have produced novel tests and treatment options for women with breast cancer. Relatively little is known about the process by which such tests are adopted into oncology practice. The objectives of the present study were to understand the experiences of medical oncologists with multigene expression profile (gep) tests, including their adoption into practice in early-stage breast cancer, and the perceptions of the oncologists about the influence of test results on treatment decision-making. METHODS: We conducted a qualitative descriptive study involving interviews with medical oncologists from academic and community cancer centres or hospitals in 8 communities in Ontario. A 21-gene breast cancer assay was used as the example of gep testing. Qualitative analytic techniques were used to identify the main themes. RESULTS: Of 28 oncologists who were approached, 21 (75%) participated in the study [median age: 43 years; 12 women (57%)]. Awareness and knowledge of gep testing were derived from several sources: international scientific meetings, participation in clinical studies, discussions with respected colleagues, and manufacturer-sponsored meetings. Oncologists observed that incorporating gep testing into their clinical practice resulted in several changes, including longer consultation times, second visits, and taking steps to minimize treatment delays. Oncologists expressed divergent opinions about the strength of evidence and added value of gep testing in guiding treatment decisions. CONCLUSIONS: Incorporation of gep testing into clinical practice in early-stage breast cancer required oncologists to make changes to their usual routines. The opinions of oncologists about the quality of evidence underpinning the test affected how much weight they gave to test results in treatment decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".