Access to Personalized Medicine: Factors Influencing the Use and Value of Gene Expression Profiling in Breast Cancer Treatment
Bibliographic record
Abstract
UNLABELLED: Genomic information is increasingly being used to personalize health care. One example is gene expression profiling (gep) tests, which estimate recurrence risk to inform chemotherapy decisions in breast cancer. Recently, gep tests were publicly funded in Ontario. We explored the perceived utility of gep tests, focusing on the factors influencing their use and value in treatment decision-making by patients and oncologists. METHODS: We conducted interviews with oncologists (n = 14) and interviews and a focus group with early-stage breast cancer patients (n = 28) who underwent gep testing. Both groups were recruited through oncology clinics in Ontario. Data were analyzed using the content analysis and constant comparison techniques. RESULTS: Narratives from patients and oncologists provided insights into various factors facilitating and restricting access to gep. First, oncologists are positioned as gatekeepers of gep, providing access in medically appropriate cases. However, varying perceptions of appropriateness led to perceived inequities in access and negative impacts on the doctor-patient relationship. Second, media attention facilitated patient awareness of gep, but also complicated gatekeeping. Third, the dedicated administration attached to gep was burdensome and led to long waits for results and also to increased patient anxiety and delayed treatment. Collectively, because of barriers to access, those factors inadvertently heightened the perceived value of gep for patients relative to other prognostic indicators. CONCLUSIONS: Our study delineates the factors facilitating and restricting access to gep, and highlights the roles of media and organization of services in the perceived value and utilization of gep. The results identify a need for administrative changes and practice guidelines to support streamlined and standardized use of gep tests.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".