Rendering the 3 + 3 Design to Rest: More Efficient Approaches to Oncology Dose-Finding Trials in the Era of Targeted Therapy
Why this work is in the frame
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Bibliographic record
Abstract
Selection of the maximum tolerated dose (MTD) as the recommended dose for registration trials based on a dose-escalation trial using variations of an MTD/3 + 3 design often occurs in the development of oncology products. The MTD/3 + 3 approach is not optimal and may result in recommended doses that are unacceptably toxic for many patients and in dose reduction/interruptions that might have an impact on effectiveness. Instead of the MTD/3 + 3 approach, the authors recommend an integrated approach. In this approach, typically an adaptive/Bayesian model provides a general framework to incorporate and make decisions for dose escalation based on nonclinical data, such as animal efficacy and toxicity data; clinical data, including pharmacokinetics/pharmacodynamics data; and dose/exposure-response data for efficacy and safety. To improve dose-ranging trials, model-based estimation, rather than hypothesis testing, should be used to maximize and integrate the information gathered across trials and doses. This approach may improve identification of optimal recommended doses, which can then be confirmed in registration trials. Clin Cancer Res; 22(11); 2623-9. ©2016 AACR SEE ALL ARTICLES IN THIS CCR FOCUS SECTION, "NEW APPROACHES FOR OPTIMIZING DOSING OF ANTICANCER AGENTS".
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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 | 0.363 | 0.832 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.008 |
| 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 it