A prospective clinical utility study of the impact of the 21-gene recurrence score assay (Onco<i>type</i> DX) in estrogen receptor positive (ER+) node negative (pN0) breast cancer in academic Canadian centers.
Bibliographic record
Abstract
549^ Background: The Oncotype DX 21-gene Recurrence Score assay (RS) can potentially predict the magnitude of chemotherapy benefit in patients with stage I-II, node-negative, ER+ disease who will be treated with tamoxifen for 5 years. While use in the United States has grown significantly since its introduction, it is not yet routinely ordered by oncologists in most parts of Canada. The primary purpose of this study was to measure the impact of the Oncotype DX test on the physician’s treatment recommendation in ER+ pN0 breast cancer in British Columbia. Methods: After providing informed consent, patients and medical oncologists completed respective pre-RS questionnaires indicating their treatment preferences and level of confidence and a decisional conflict scale (patients only). At a subsequent visit, after the RS result was known and discussed, the patient and oncologist completed a second set of questionnaires. The proportion of physician treatment recommendations that changed from baseline to follow-up (post RS) were calculated with 95% confidence interval (CI). A prospective health economic (HE) analysis was also performed. Results: From May 2010 to July 2011, two participating BCCA centres enrolled 156 patients. Of the 150 for whom successful RS assay results were obtained, physicians changed their chemotherapy recommendation in 45 cases (30%; 95% CI 22.8-38.0%); either to add (10%; 95% CI 5.7-16.0%) or omit (20%; 95% CI 13.9-27.3%) adjuvant chemotherapy. As a secondary end-point, in 84 cases (56%; 95% CI 47.7-64.1%) there was a change in either the planned chemo and/or endocrine therapy recommendation. There was an overall significant improvement in physician confidence post RS (p < 0.001). Patient decisional conflict also significantly decreased following the RS assay (p < 0.001). The HE analysis is ongoing and will be presented separately. Conclusions: Within the context of a publicly funded health care system, the RS assay significantly affects adjuvant treatment recommendations in ER+ node negative breast cancer, in addition to reducing patient decisional conflict.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".