Patients’ Perceptions of Gene Expression Profiling in Breast Cancer Treatment Decisions
Bibliographic record
Abstract
INTRODUCTION: Determining the likely benefit of adjuvant chemotherapy for early-stage breast cancer patients depends on estimating baseline recurrence risk. Gene expression profile (gep) testing of tumours informs risk prediction, but evidence of its clinical utility is limited. We explored patient perceptions of gep testing and the impact of those perceptions on chemotherapy decisions. METHODS: We conducted one focus group (n = 4) and individual interviews (n = 24) with patients who used gep testing, recruited through clinics at two hospitals in Ontario. Data were analyzed using content analysis and constant comparison techniques. RESULTS: Patients' understanding of gep testing was variable, and misapprehensions were common. Patients valued the test because it provided them with certainty amidst confusion, with options and a sense of empowerment, and with personalized, authoritative information. They commonly believed that the test was better and fundamentally different from other clinical tests, attributing to it unique power and truth-value. This kind of "magical thinking" was derived from an amplified perception of the test's validity and patients' need for reassurance about their treatment choices. Despite misperceptions or magical thinking, gep was widely considered to be the deciding factor in treatment decisions. CONCLUSIONS: Patients tend to overestimate the truth-value of gep testing based on misperceptions of its validity. Our results identify a need to better support patient understanding of the test and its limitations. Findings illustrate the deep emotional investment patients make in gep test results and the impact of that investment on their treatment decisions.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".