Does RSClin provide additional information over classic clinico-pathologic scores (PREDICT 2.1, INFLUENCE 2.0, CTS5)?
Bibliographic record
Abstract
PURPOSE: Few studies have compared the performance of gene-expression profiling tests (e.g. Oncotype-Dx) to clinico-pathologic risk calculators (e.g. PREDICT 2.1, INFLUENCE 2.0, and CTS5) or tools that combine both (e.g. RSClin) in patients with early breast cancer (EBC). A large trial dataset was used to evaluate the prognostic performance of different tests based on patient outcomes. METHODS: The TEAM pathology cohort accrued samples from 4736 postmenopausal hormone positive women with EBC, treated with either exemestane or tamoxifen followed by exemestane. Oncotype-Dx-trained risk scores were previously generated by gene-expression profiling. Patient data was used to calculate various recurrence scores. Analysis was restricted to the N0/N1 population and prognostic ability of selected risk tools was assessed using Cox regression analysis and Harrell's C-statistic. RESULTS: Results were available for 2065 patients. There was low correlation between PREDICT 2.1 (r = -0.12), INFLUENCE 2.0 (r = 0.20), CTS5 (r = 0.16) with Oncotype-Dx-trained results. In N0 patients, RSClin had improved prognostic ability (C-statistic = 0.66) on DMFS compared to PREDICT 2.1 (0.60), INFLUENCE 2.0 (0.57), CTS-5 (0.62), and Oncotype-Dx (0.63). CONCLUSION: Combining molecular and clinico-pathologic factors enhances prognostic information. However, the impact of this on actual patient management requires further prospective validation. The trial is registered with clinicaltrials.gov NCT00279448 and NCT00032136; with Netherlands Trial Register, number NTR 267; and the Ethics Commission Trial, number 27/2001.
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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".