OPTIMA prelim: Optimal personalized treatment of early breast cancer using multiparameter analysis: Preliminary study.
Bibliographic record
Abstract
TPS665 Background: “Multi-parameter” prognostic tests such as Oncotype DX are increasingly used to identify women with ER +ve HER2 -ve breast cancer treated with endocrine therapy who are unlikely to benefit meaningfully from adjuvant chemotherapy. The supporting evidence for predictive testing is retrospective and is strongest for women with negative nodes. Randomised trials to validate testing are in progress but more evidence is needed, especially for node positive disease. There is early evidence that other multi-parameter tests which are in development have a predictive utility. Methods: OPTIMA will assess the value of multi-parameter tests in a UK population. OPTIMA prelim, the feasibility phase, will recruit 300 patients from May 2012 (with a 200 patient bridging extension to the main study). Eligible patients will have ER +ve HER2 -ve tumours with involved nodes (pN1-2). Patients will be randomised to the standard arm of both chemotherapy and endocrine therapy, or to the “test-directed treatment” arm in which patients will be assigned either the same chemotherapy and endocrine therapy or endocrine therapy only according to the result of an Oncotype DX test. OPTIMA prelim has 3 objectives. (1) To establish whether randomisation to test-directed treatment is acceptable to patients and clinicians. This will be done through qualitative research with in-depth interviews with OPTIMA researchers and by recording and analysing consultations when the trial is discussed with potential participants. (2) Alternative multi-parameter tests will be evaluated on all patients’ tumours and their performance will be compared to Oncotype DX using statistical and cost-effectiveness analysis to select a candidate test(s) appropriate for NHS use to be evaluated in the main trial. (3) The success of the main trial depends on efficient tumour sample collection and analysis to avoid treatment delays. The obstacles to this will be analysed in detail using a sample tracking database. The main study is an efficacy trial of 2-3 arms with the same design but uses tests selected in OPTIMA prelim. It will compare 5-year relapse free survival of test-directed vs. conventional treatment with a non-inferiority hypothesis.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".