OPTIMA prelim: Optimal personalized treatment of early breast cancer using multiparameter tests.
Bibliographic record
Abstract
TPS656 Background: Chemotherapy may have little effect on some subtypes of early breast cancer, identified as being hormonally responsive tumours without HER2 gene amplification and with a low or intermediate grade. Multi-parameter genomic tests such as Oncotype DX are increasingly used to identify patients for whom the addition of chemotherapy may confer little additional benefit. OPTIMA has an adaptive design seeking to advance development of personalised medicine in breast cancer by assessing the value of multi-parameter tests in a UK population of intermediate risk. Methods: OPTIMA prelim, the feasibility phase, has 3 objectives: (1) To evaluate performance and health-economics of multi-parameter tests to determine which test(s) will be used in the main trial; (2) To establish efficient and timely sample collection and analysis essential to deliver multi-parameter test driven treatment; (3) To establish the acceptability to patients and clinicians of randomisation to test-directed treatment assignment. OPTIMA prelim aims to recruit 300 patients with ER+ve HER2-ve tumours with involved nodes (pN1-2). Patients are randomized to the standard arm of chemotherapy with endocrine therapy, or to the “test-directed treatment” arm assigned to either the same chemotherapy with endocrine therapy or endocrine therapy only according to the result of an Oncotype DX test. The decision to continue to a main trial will be determined by concordance, cost and willingness of patients to be randomized to test guided treatment. Cost-effectiveness models will be based on the model developed in preparation for the OPTIMA trial, updated with contemporary evidence from the feasibility study and appropriate external data, e.g. the Ontario prospective cohort study. Results: Optima opened in Sept 2012 with 25 centres involved. To date 22 patients are registered, of which 17 have been randomised. Patient focus groups show the trial design is acceptable and has potential to reduce need for chemotherapy. TSC and DMEC agree that this is an important trial testing the feasibility within this patient population. Decision rules are challenging for this study but employment of adaptive designs gives the flexibility needed for the main trial. Clinical trial information: ISRCTN42400492.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".