Prediction of Oncotype Dx recurrence score using clinical parameters
Bibliographic record
Abstract
OBJECTIVE/BACKGROUND: The Oncotype Dx test is a genomic test currently used in clinical practice to predict the risk of disease recurrence in estrogen receptor (ER)-positive, HER2-negative breast cancer patients with axillary lymph node-negative or micrometastatic disease. The test is one of several similar genomically based tests available. Although it has a good predictive value, it is expensive and thus constitutes a significant financial burden for health systems. Thus, several attempts have been made to devise low-cost tools that could predict the recurrence score derived from the genomic evaluation using easily obtainable clinical parameters. METHODS: Two previously proposed predictive tools were evaluated in a cohort of 201 patients that had undergone the Oncotype Dx test for their efficacy in predicting the Oncotype Dx Recurrence Score (RS). A simple predictor, named GR-PR, based on two available pathologic parameters, grade and progesterone receptor status was devised and also evaluated. RESULTS: The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of all three tools were compared and found to be similar for all cutoff points of Oncotype Dx RS. The accuracy of GR-PR was comparable to the best performing of the two other prediction tools for all four cutoff points. CONCLUSION: The simple GR-PR predictor proposed in this study seems to be at least as accurate as more complex tools and should be the preferred tool for the prediction of Oncotype Dx RS from clinicopathologic parameters when the Oncotype Dx test is not available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".