Does integration of Magee equations into routine clinical practice affect whether oncologists order the Oncotype DX test? A prospective randomized trial
Bibliographic record
Abstract
OBJECTIVE: The three Magee Equations provide an estimate of the Oncotype DX recurrence score using commonly available clinicopathologic information (tumour size, grade, oestrogen receptor, progesterone receptor, HER2, and Ki67). We assessed whether integration of Magee Equations into routine clinical practice affected the frequency of Oncotype DX requests. METHODS: Patients with newly diagnosed, node negative, hormone receptor positive, and HER2 negative invasive breast cancer were randomized to undergo a Magee calculation or not. At the first clinic assessment, the oncologist was provided with all routinely available clinicopathologic information (including Ki67) either with or without the results of Magee Equations. Primary outcome was frequency of Oncotype DX ordering. Secondary outcomes included frequency of chemotherapy use, time to commencement of radiotherapy, or systemic therapy. Physician comfort with systemic therapy choices and the use of Ki67 and Magee Equations was also assessed. RESULTS: Data from 175 randomized patients was available, 84 patients (48%) with and 91 (52%) without calculated Magee Equations. Oncotype DX was ordered in 10 (12.05%) and 13 (14.44%) (RR 0.83, 0.39-1.80; P = 0.64) in the Magee and no Magee groups, respectively. There were no statistically or clinically significant differences between the randomized groups for any of the secondary outcomes. Availability of both Ki67 and Magee Equations was associated with increased physician comfort around systemic treatment decisions. CONCLUSIONS: In a practice where Ki67 is routinely available, addition of Magee Equations into routine clinic practice was not associated with a reduction in Oncotype DX use. Availability of both Ki67 and Magee Equations did however increase physician comfort with systemic therapy decisions.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".