Prognosis, treatment decision-making and value: A qualitative exploration of the emerging role of breast cancer prognostic assays
Bibliographic record
Abstract
BACKGROUND: Breast cancer prognostic assays are emerging as 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 effect on prognosis and treatment for both providers and patients. The objective of this study was to explore perspectives on the use and integration of breast cancer prognostic assays in clinical care. METHODS: 15 international researcher-physician/scientist key opinion leaders who had conducted studies on breast cancer prognostic assays were interviewed. Participants had conducted studies using five different 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 use and value of these assays. The emerging role of prognostic assays to identify overtreatment and unnecessary care was highlighted by the majority of 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 provided insights into deliberate efforts to integrate the assays into clinical practice and how improved quality of life and reduction in overtreatment was positioned to justify high cost of the assay. Finally, participants 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. CONCLUSIONS: The results of this study provide insights into the integration of prognostic assays into healthcare services. The assays are seeking to extend the boundaries of their clinical utility through identifying overtreatment and low value care. Efforts to integrate these assays and justify their high prices are unpacked and reveal complex and contradictory factors. Finally, these results illuminate that the varied approaches to cancer treatment, and varied use of chemotherapy create disparate perceptions of value for the assays.
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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 | 0.034 | 0.040 |
| 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 | 0.017 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".