Does Pre-Emptive Availability of PREDICT 2.1 Results Change Ordering Practices for Oncotype DX? A Multi-Center Prospective Cohort Study
Bibliographic record
Abstract
For early-stage hormone receptor (HR)-positive and HER2-negative breast cancer, tools to estimate treatment benefit include free and publicly available algorithms (e.g., PREDICT 2.1) and expensive molecular assays (e.g., Oncotype DX). There remains a need to identify patients who de-rive the most benefit from molecular assays and where this test may be of poor value. In this multicenter prospective cohort study, we evaluated whether use of PREDICT 2.1 would impact physician decision making. For the first 6 months of the study, data on physician use of both PREDICT 2.1 and Oncotype DX ordering were collected on all newly diagnosed patients eligible for molecular testing. After 6 months, an educational intervention was undertaken to see if providing physicians with PREDICT 2.1 results affects the frequency of Oncotype DX requests. A total of 602 patients across six cancer centers in Ontario, Canada were recruited between March 2020 and November 2021. Providing PREDICT 2.1 results and an educational intervention did not alter the ordering of an Oncotype DX. For patients with low clinical risk, either by clinico-pathologic features or by PREDICT 2.1, the probability of obtaining a high Oncotype DX recurrence score was substantially lower compared to patients with high-clinical-risk disease. The introduction of an educational intervention had no impact on molecular assay requests. However, routine ordering of molecular assays for patients with low-clinical-risk disease is of poor value.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".