Multigene Expression Profile Testing in Breast Cancer: Is There a Role for Family Physicians?
Bibliographic record
Abstract
BACKGROUND: Family physicians (fps) play a role in aspects of personalized medicine in cancer, including assessment of increased risk because of family history. Little is known about the potential role of fps in supporting cancer patients who undergo tumour gene expression profile (gep) testing. METHODS: We conducted a mixed-methods study with qualitative and quantitative components. Qualitative data from focus groups and interviews with fps and cancer specialists about the role of fps in breast cancer gep testing were obtained during studies conducted within the pan-Canadian canimpact research program. We determined the number of visits by breast cancer patients to a fp between the first medical oncology visit and the start of chemotherapy, a period when patients might be considering results of gep testing. RESULTS: The fps and cancer specialists felt that ordering gep tests and explaining the results was the role of the oncologist. A new fp role was identified relating to the fp-patient relationship: supporting patients in making adjuvant therapy decisions informed by gep tests by considering the patient's comorbid conditions, social situation, and preferences. Lack of fp knowledge and resources, and challenges in fp-oncologist communication were seen as significant barriers to that role. Between 28% and 38% of patients visited a fp between the first oncology visit and the start of chemotherapy. CONCLUSIONS: Our findings suggest an emerging role for fps in supporting patients who are making adjuvant treatment decisions after receiving the results of gep testing. For success in this new role, education and point-of-care tools, together with more effective communication strategies between fps and oncologists, are needed.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".