39 Prognosis, treatment decision-making and value: a qualitative exploration of provider perspectives on breast cancer genomic assays
Bibliographic record
Abstract
Background Breast cancer genomic assays are emerging as essential tools used by physicians in the cancer treatment decision-making process. This technology is new, and we must interrogate the integration of these assays into clinical practice and their impact on prognosis and treatment for both providers and patients. The objective of this study was to explore provider’s perspectives on the use and integration of breast cancer genomic assays in clinical care. Methods 15 international physicians/researchers who had conducted studies on breast cancer genomic assays were interviewed. Participants had conducted studies across five different genomic assays. The interview guide was developed through a literature review and leveraged extensive data collected on key clinical utility outcomes for the assays. All interviews were conducted virtually, recorded, and transcribed verbatim. Data were analysed using thematic analysis. Results Three novel themes emerged from participant’s perspectives on the integration of these assays into clinical practice. The emerging role of genomic assays to identify overtreatment and unnecessary care was highlighted by participants. The primary value of these tools is to identify patients who will not benefit from adjuvant chemotherapy. Participants reported that current standard practice is to overtreat and portrayed the binary or definitive results of these assays as an important tool to reduce overtreatment. Participants also reported how the perspectives and uses of these assays vary significantly in different countries and cultures. This jurisdictional variation in cancer prognosis and treatment was observed as producing uneven and sometimes problematic interpretations of value for the assays. Finally, participants provided insights into industry’s deliberate efforts to integrate the assays into clinical practice and their significant role in marketing and evidence production. The participants detailed how this evidence of improved quality of life and reduction in overtreatment was positioned to justify a premium pricing strategy. Conclusions The results of this study provide insights into the integration of genomic assays into clinical decision-making processes. The assays are extending the boundaries of their clinical utility through identifying overtreatment and unnecessary care. These results also illuminate the subjectivity of cancer treatment and the contested space within which these tools attempt to add value. Finally, perspectives on industry’s efforts to legitimize these assays, their high prices and situate them as an integral component in the cancer care decision-making process.
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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 | 0.032 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".