The value of personalizing medicine: Medical oncologists’ and patients’ perspectives on genomic testing of breast tumors in chemotherapy treatment decisions.
Bibliographic record
Abstract
e11000 Background: Benefits of adjuvant chemotherapy for early-stage breast cancer patients (pts) depend on baseline recurrence risks. Gene expression profiling (GEP) of tumours informs baseline risk prediction, potentially reducing unnecessary treatment and healthcare costs. Limited evidence exists on its clinical utility; we explored pts and medical oncologists (MO) perspectives on GEP in chemotherapy decisions. Methods: We used a qualitative design of individual interviews with MO (n=10), focus groups and individual interviews with pts (n=20) from Ontario. Pts who underwent genomic testing of their tumours (‘OncotypeDx’), were recruited through clinics from two academic hospitals in Toronto. MO were recruited through clinics, advertisements and referrals from the research team. Data were analyzed using interpretative qualitative methods, including content analysis, qualitative description and constant comparison techniques. Results: Pts and MO valued GEP as an additional decision-support tool, complementing existing clinical indicators. Its perceived utility varied between pts and MO. Pts valued the test highly, suggesting it was one of the primary determinants of their treatment decision. All pts followed the course of action their results suggested. Pts with intermediate scores often used the results to reinforce their pre-existing treatment preferences. MO were mixed about the test’s utility. Some considered it another tool supporting their approach to risk assessments, while others used it more definitively to resolve their uncertainty. MO explained the test’s contribution to decision-making but remained uncertain about pts understanding and expectations of the test. Some raised concerns about the variability of its use and interpretation within their medical community. Conclusions: Pts and MO valued the test, often using it as a primary determinant in their treatment decision, despite MO concerns about its technical limitations and pts limited understanding. Results identify a need for informational decision aids and practice guidelines to support pts understanding and standardized application 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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 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".