Access to personalized medicine: Factors influencing the use and value of gene expression profiling in treatment decision making.
Bibliographic record
Abstract
10 Background: Genomic information is increasingly used to personalize health care. One example is gene-expression profiling (GEP) tests that estimate recurrence risk to inform chemotherapy decisions in breast cancer treatment. Recently, GEP tests were publicly funded in Ontario. We assessed the clinical utility of GEP tests, exploring the factors facilitating their use and value in treatment decision-making. Methods: As part of a mixed-methods clinical utility study, we conducted interviews with oncologists (n=14), and focus groups and interviews with breast cancer patients (n=28) who underwent GEP, recruited through oncology clinics in Ontario. Data were analyzed using content analysis and constant comparison. Results: Various factors governing access to GEP have given rise to challenges for patients and oncologists. 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. Media attention facilitated patient awareness of GEP but complicated gatekeeping. Additional administration burden and long waits for results led to increased patient anxiety and delayed treatment. Collectively, these factors inadvertently heightened GEP’s perceived value for patients relative to other prognostic indicators because of barriers to access. Conclusions: This study delineates the factors facilitating and restricting access to GEP, and highlights the roles of the media and organization of services in GEP’s perceived value and utilization. Results identify a need for administrative changes and practice guidelines to support streamlined and standardized utilization of the test.
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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 | 0.006 | 0.049 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".