The Value of Personalizing Medicine: Medical Oncologists’ Views on Gene Expression Profiling in Breast Cancer Treatment
Bibliographic record
Abstract
OBJECTIVES: Guidelines recommend gene-expression profiling (GEP) tests to identify early-stage breast cancer patients who may benefit from chemotherapy. However, variation exists in oncologists' use of GEP. We explored medical oncologists' views of GEP tests and factors impacting its use in clinical practice. METHODS: We used a qualitative design, comprising telephone interviews with medical oncologists (n = 14; 10 academic, 4 in the community) recruited through oncology clinics, professional advertisements, and referrals. Interviews were analyzed for anticipated and emergent themes using the constant comparative method including searches for disconfirming evidence. RESULTS: Some oncologists considered GEP to be a tool that enhanced confidence in their established approach to risk assessments, whereas others described it as "critical" to resolving their uncertainty about whether to recommend chemotherapy. Some community oncologists also valued the test in interpreting what they considered variable practice and accuracy across pathology reports and testing facilities. However, concerns were also raised about GEP's cost, overuse, inappropriate use, and over-reliance on the results within the medical community. In addition, although many oncologists said it was simple to explain the test to patients, paradoxically, they remained uncertain about patients' understanding of the test results and their treatment implications. CONCLUSION: Oncologists valued the test as a treatment-decision support tool despite their concerns about its cost, over-reliance, overuse, and inappropriate use by other oncologists, as well as patients' limited understanding of GEP. The results identify a need for decision aids to support patients' understanding and clinical practice guidelines to facilitate standardized use of the test.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".